ASSESSING WHITEBARK PINE VIGOR AND

FACILITATION ROLES IN THE ALPINE TREELINE ECOTONE

by

SARAH C. BLAKESLEE

B.S. Biology, University of Colorado Colorado Springs, 2008

A thesis submitted to the

Faculty of the Graduate School of the

University of Colorado in partial fulfillment

of the requirements for the degree of

Master of Science

Biology

2012

ii

This thesis for the Master of Science degree by

Sarah C. Blakeslee

has been approved for the

Department of Integrative Biology

by

Diana F. Tomback, Chair

Michael B. Wunder

Leo P. Bruederle

16 November, 2012

iii

Blakeslee, Sarah, C. (M.S., Department of Integrative Biology Master of Science)

Assessing Whitebark Pine Vigor and Facilitation Roles in the Alpine Treeline Ecotone

Thesis directed by Professor Diana F. Tomback

ABSTRACT

Whitebark pine (Pinus albicaulis) is an upper subalpine and treeline conifer of the

higher mountains of the western United States and Canada. At treeline on the Eastern

Front of the Rocky Mountains, whitebark pine appears to facilitate tree island

development. It is currently declining at treeline from infection by white pine blister rust,

caused by Cronartium ribicola. We are studying how whitebark pine facilitates tree

island formation and how blister rust mortality may affect these processes in two treeline

study areas in Montana: Divide Mountain, Glacier National Park and Blackfeet Indian

Reservation; and Line Creek Research Natural Area, Custer National Forest. We tested

three hypotheses: 1) Whitebark pine is hardier than other treeline conifer species, as

demonstrated by more vigorous growth and survival at treeline, 2) whitebark pine

provides more favorable leeward microsites for tree island recruitment than other conifers

or microsites, and 3) death of windward whitebark pine in established tree islands leads

to vigor loss in leeward conifers.

We found support for each hypothesis. Whitebark pine was significantly more

numerous than both spruce (Picea engelmannii) and fir (Abies lasiocarpa) among solitary

trees. Solitary, krummholz whitebark pine trees produced significantly longer annual

shoots than both spruce and fir, indicating faster branch growth and canopy area increase

under harsh conditions. These results indicate higher vigor and potentially higher

survival rate than spruce and fir. Germinated spruce seeds had higher summer survival,

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and planted fir and spruce seedlings had greater vigor, when leeward of whitebark pine

compared to spruce, rock, or exposed microsites, suggesting that whitebark pine

microsites provided more protection. In established tree islands, the presence of a

windward whitebark pine was associated with greater general vigor, longer shoot lengths,

and lower shoot mortality in leeward trees than under experimental conditions where the

windward whitebark pine was girdled and defoliated. Because whitebark pine is better

able to survive and grow in the alpine treeline ecotone than other conifer species, this

may, in part, explain its greater prevalence. Whitebark pine is more likely to facilitate

tree island development, and provide a better microsite for seedling establishment.

The form and content of this abstract are approved. I recommend its publication.

Approved: Diana F. Tomback

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DEDICATION

I dedicate this work to Logan, in appreciation of all his love and support.

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ACKNOWLEDGMENTS

I would like to thank my advisor Dr. Diana F. Tomback for her willingness to

share her knowledge and experience and for assisting me throughout this entire process.

Also invaluable to this work are Jill C. Pyatt and Libby R. Pansing, who provided

outstanding support both in the field and back in lab. I am also appreciative of the field

assistance provided by Logan Wealing, Soledad Diaz, and Aaron Wagner. I thank my

committee members Dr. Leo P. Bruederle and Dr. Michael Wunder for their advice and

assistance throughout this project. A special thank-you goes to our collaborators in the

Dr. Lynn Resler and Dr. George Malanson labs because without them this project never

would have been possible. I also appreciate the help provided by numerous folks and

various agencies, especially Kent Houston of the Shoshone National Forest, Custer

National Forest, the Blackfeet Tribal Nation, Colorado State Forest Service Nursery,

Glacier National Park, and the ecology and evolutional biology group members at CU-

Denver.

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TABLE OF CONTENTS

CHAPTER

I. WHITEBARK PINE BACKGROUND .......................................................................... 1

Taxonomy and Distribution .................................................................................... 1

Whitebark Pine Seeds: Seed Dispersal and Food Source ....................................... 1

The Alpine-Treeline Ecotone and Whitebark Pine’s Roles .................................... 3

Threats to Whitebark Pine ...................................................................................... 6

Global Climate Change and Treeline Impacts ........................................................ 9

Figures and Tables ................................................................................................ 12

II. INTRODUCTION ........................................................................................................ 14

Background ........................................................................................................... 14

Conceptual Framework ......................................................................................... 17

Hypotheses for Testing ......................................................................................... 18

Figures and Tables ................................................................................................ 21

III. METHODS ................................................................................................................. 22

Study Areas ........................................................................................................... 22

Relative Vigor Study............................................................................................. 23

Field Methods. ................................................................................................ 24

Data Analysis. ................................................................................................. 26

Planting and Sowing Study ................................................................................... 28

Field Methods. ................................................................................................ 28

Data Analysis. ................................................................................................. 30

Girdling Study ....................................................................................................... 31

Field Methods. ................................................................................................ 31

Data Analysis. ................................................................................................. 32

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Figures and Tables ................................................................................................ 34

IV. RESULTS ................................................................................................................... 40

Relative Vigor Study............................................................................................. 40

Transects ......................................................................................................... 40

Shoot Lengths and Shoot Growth Rate Comparisons..................................... 41

Other Small Tree Measurements. ................................................................... 43

Results Summary. ........................................................................................... 44

Planting and Sowing Study ................................................................................... 44

Seedling Survival. ........................................................................................... 44

Seed Germination and Summer Survival. ....................................................... 46

Results Summary. ........................................................................................... 47

Girdling Study ....................................................................................................... 48

Leeward Conifer Vigor ................................................................................... 48

Shoot Lengths. ................................................................................................ 48

Shoot Mortality. .............................................................................................. 49

Results Summary. ........................................................................................... 50

Figures and Tables ................................................................................................ 51

V. SYNTHESIS AND DISCUSSION .............................................................................. 61

Study Conclusions. ............................................................................................... 61

Potential Implications for Whitebark Pine Decline at Treeline ............................ 65

Figures and Tables ................................................................................................ 69

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REFERENCES ................................................................................................................. 70

APPENDIX

I. Small Tree Measurements ............................................................................................ 77

II. Small Tree Analyses..................................................................................................... 80

III. Planting Study Microsite Heights ............................................................................... 82

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LIST OF TABLES

Table

I.1 US and Canadian conifers susceptible to white pine blister rust infection ................ 13

III.1 Qualitative vigor categories ..................................................................................... 35

III.2 Sample sizes of small krummholz trees in the relative vigor study ......................... 35

IV.1 Species abundances of solitary conifers in transects ............................................... 52

IV.2 Krummholz shoot lengths ........................................................................................ 53

IV.3 Krummholz tree shoot growth rates ........................................................................ 54

IV.4 Upright upper subalpine conifer shoot lengths ........................................................ 55

IV.5 Upright shoots with minimum needle lengths subtracted........................................ 56

IV.6 Small shoot lengths vs. upright shoot lengths: proportions ..................................... 57

IV.7 Summer 2012 survival advantage and relative death risk of seed germinants on

Divide Mountain ............................................................................................................... 59

AI.1 Small Tree Measurement Summaries ....................................................................... 77

AI.2 Divide Mountain Small Tree Measurements by Site ............................................... 78

AII.1 Change in krummholz tree stem diameters ............................................................. 80

AII.2 Krummholz tree canopy areas ................................................................................ 81

AII.3 Krummholz tree heights .......................................................................................... 81

AIII.1 Planting and sowing study microsite heights ......................................................... 82

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LIST OF FIGURES

Figure

I.1 Distribution of Pinus albicaulis in North America .................................................... 12

I.2 Image of active blister rust stem canker on an infected whitebark pine. .................... 13

II.1 Overall conceptual model.......................................................................................... 21

III.1 Research study areas ................................................................................................ 34

III.2 Planted seedlings at the Line Creek RNA ................................................................ 36

III.3 Germinated seeds on Divide Mountain.................................................................... 37

III.4 Example of before and after girdling and defoliation treatment .............................. 38

III.5 Leeward shoot vs. exposed shoot sampling areas .................................................... 39

IV.1 Solitary krummholz tree density by species on Divide Mountain and Line Creek

RNA .................................................................................................................................. 51

IV.2 One year post-planting seedling survival per microsite .......................................... 58

IV.3 2012 Divide Mountain seed germination counts ..................................................... 59

IV.4 Girdling Study leeward conifer shoot length trends over time ................................ 60

V.1 Potential consequences of blister rust to alpine treeline dynamics. .......................... 69

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LIST OF ABBREVIATIONS

1. ATE Alpine Treeline Ecotone – the region between the subalpine forest

and alpine tundra where conifers are krummholz or dwarfed

2. SF Subalpine Fir (Abies lasiocarpa)

3. WP Whitebark Pine (Pinus albicaulis)

4. ES Engelmann Spruce (Picea engelmannii)

5. RNA Research Natural Area; in reference to the Line Creek Natural

Area located on the Beartooth Plateau, Montana

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CHAPTER I.

WHITEBARK PINE BACKGROUND

Taxonomy and Distribution

Whitebark pine (Pinus albicaulis) is one of several stone pines comprising Pinus,

subgenus Strobus, section Strobus, subsection Cembrae (Price et al, 1998). Since

monophyly of Subsection Cembrae is unsubstantiated, it has been proposed that

subsection Cembrae be merged with subsection Strobi into a new subsection Strobus

(Liston et al, 1999; Gernandt et al, 2005). This classification has yet to be officially

recognized. The stone pines of subsection Cembrae are characterized as having five

needles per fascicle and indehiscent cones with wingless seeds that are dispersed by

nutcrackers (Nucifraga spp.) (McCaughey and Schmidt, 2001).

Whitebark pine is distributed from the southern Sierra Nevada of California north

through the Cascade and coastal ranges into British Columbia; and from the Greater

Yellowstone region of Wyoming north through the Rocky Mountains of British

Columbia and Alberta Canada (Figure I.1). Whitebark pine is limited to upper subalpine

and treeline forests in high elevation mountains from 37◦ to 55

◦N (Arno and Hoff, 1990).

It is often a dominant treeline species, except at its most northern limits and in the

snowiest regions of the southern Canadian Rockies and coastal ranges (Arno and

Hammerly, 1984). Whitebark pine assumes a krummholz growth form at treeline in the

drier mountain ranges (Arno and Hammerly, 1984).

Whitebark Pine Seeds: Seed Dispersal and Food Source

Because whitebark pine has indehiscent cones with wingless seeds, it relies on a

co-evolved mutualism with Clark’s nutcracker (Nucifraga columbiana) for seed dispersal

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(Tomback, 1982). It is possible that this method of seed dispersal evolved as a

consequence of both genetic drift in small populations and seed selection choice by

Clark’s nutcracker (Tomback and Linhart, 1990). Every year from late summer to early

fall, these birds gather seeds from cones, carry them within their sublingual pouch, and

cache them throughout the subalpine and treeline terrain. In many regions nutcrackers

typically select for seed caching steep, south facing slopes that accumulate minimal

snowpack (Tomback, 1982). Distances from the cache to the original seed source can

vary from a few meters up to 29 km in distance and 307 meters in elevation (Lorenz and

Sullivan, 2009). The seeds that are not later consumed germinate, thereby regenerating

the species.

Resler (2004) observed whitebark growing at treeline and found that many cache

sites selected by Clark’s nutcracker were sheltered and important for whitebark pine

seedling survival. These microsites, which are often terraces or boulders, may facilitate

the germination and growth of whitebark pine seedlings. Cache sites selected by Clark’s

nutcracker and the natural hardiness of whitebark pine account for a large majority of the

spatial distribution and population genetic structure of the species (Tomback, 2001).

Seeds from whitebark pine cones are also an important food source for other

wildlife. Grizzly bears (Ursus arctos) and pine squirrels (Tamiasciurus spp.) rely on

these seeds (Mattson et al, 1992; McKinney and Fiedler, 2010). In the Greater

Yellowstone Area, pine squirrels store cones in middens that are later raided by the bears.

These seeds are a large part of the grizzly’s food source in the spring and summer of

good cone crop years (Matson and Reinhart, 1994). Other wildlife species that consume

these seeds include small mammals, such as chipmunks (Tamius spp.), and golden mantle

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ground squirrels (Spermophilus lateralis); and some small birds, such as woodpeckers,

nuthatches, finches, and Steller’s jays (Cyanocitta stelleri) (Tomback, 1978; Hutchins

and Lanner, 1982; Tomback and Kendall, 2001).

The Alpine-Treeline Ecotone and Whitebark Pine’s Roles

The transition between the subalpine forest and alpine tundra is referred to as the

alpine-treeline ecotone (ATE). This high elevation zone is characterized by krummholz

conifers, dry, windswept slopes, and cold temperatures (Marr, 1977; Arno and

Hammerly, 1984; Finklin, 1986). Grace et al (2002) describe the ‘climatological

bottleneck’ that results in krummholz growth. Trees in the alpine treeline seldom

produce cones with viable seeds, so this tree community is generated by seeds coming

from the subalpine zone. Therefore, trees in the alpine treeline ecotone must physically

adapt to survive the harsh climate (Malanson et al., 2007). Krummholz growth forms

result when wind-blasted snow and ice particles kill upright growth. Consequently, the

only branches able to survive are those that grow low to the ground. Krummholz trees

often have foliage surface temperatures 5-10° higher than ambient temperature. Taller

trees have surface temperatures 5ᶱ lower than ambient temperatures. Seedlings in the

alpine treeline ecotone may be sheltered and in favorable microclimates, but as they grow

taller, their growth rate is reduced and direction of growth altered by wind and

desiccation; thus, they become dwarfed or krummholz (Grace et al, 2002).

Survival in the alpine treeline ecotone is often increased by the formation of tree

islands. Tree islands are krummholz mats containing one or more individual trees

growing in close proximity (Marr, 1977). Solitary tree islands are comprised of one tree.

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Multi-tree islands are comprised of two or more individual trees or many branches that

growing in layered form due to adventitious roots (Benedict, 1984).

Foundation species are highly abundant ecosystem components that exert much

influence on ecosystem function and stability (Ellison et al., 2005). Keystone species

promote and support the biodiversity of their ecosystems (Soule et al, 2003). Foundation

and keystone species in forest ecosystems have the ability to maintain biotic and abiotic

ecosystem components. If a keystone or foundational species declines, there could be a

resulting trophic cascade, with a loss of biodiversity or ecosystem function (Ellison et al.,

2005). Throughout its range, whitebark pine is both a foundation and keystone species

(Tomback and Achuff, 2010). Whitebark pine acts as a facilitator or ‘nurse tree’ by

creating protective microsites for less hardy conifer species (Callaway, 1998). In the

alpine treeline ecotone, one of whitebark pine’s most important functional roles is in tree

island initiation. Whitebark pines growing in sheltered microsites can facilitate

community development by mitigating the harsh conditions on their leeward side. Resler

and Tomback (2008) found that whitebark pine was the windward tree island initiator for

nearly half the multi-tree tree islands among two study sites east of the Continental

Divide. They also found that whitebark pine was an important component of tree islands

in this region: 255 out of 266 tree islands sampled contained whitebark pine.

Research has demonstrated just how important tree islands can be for survival of

less hardy conifers. Hattenschwiler and Smith (1999) studied distributions of subalpine

fir and Engelmann spruce in the central Rocky Mountains to determine locations with

greatest survival. Although Engelmann spruce appears to germinate quickly and at lower

temperatures than subalpine fir, no seedlings of either species could survive on the

5

windward side of tree islands. In the alpine treeline ecotone, the most frequent location

for seedling establishment was on the leeward side of tree islands where snow

accumulation is maintained at a moderate depth of 0.5 – 1.5 m, thus offering protection.

Germino et al (2002) found that the microsites with windward protection were associated

with a 20% higher survival rate of Engelmann spruce seedlings. Additionally, seedling

survival was 70% higher when microsite features, such as branches, were located directly

above the seedlings. They claim that close proximity to tree islands and overhead

structures, such as branches, may moderate solar and long wave radiation, reduce

daytime temperature extremes, and maintain snowdrift accumulation. These factors

increase seedling survival by making environmental conditions more moderate.

Whitebark pine’s role in establishing tree islands is an important ecological

function, both for community development at treeline and for the provision of ecosystem

services to people (Resler and Tomback 2008). Tree islands provide important

ecosystem services. Tree islands are involved in watershed hydrology through the

maintenance of snowpack, which regulates the rate of snowmelt run-off (Holtmeier and

Broll, 1992). Conifer roots also help stabilize soil erosion (Tomback et al. 2001). If no

tree islands are present to perform these functions, erosion and summer drought may

result. Farmers and ranchers with land in the valley bottoms and on the plains,

downstream of these mountains, rely on regulated water from snowmelt to fill streams

and creek beds necessary for crops and livestock. Municipal water reservoirs are

sometimes kept at appropriate levels by snowmelt (Smith et al., 2009). Late summer

shortages could lead to rationing or the costly service of transporting water supply to the

region.

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Threats to Whitebark Pine

Currently, there are several threats to whitebark pine throughout its range. Fire

suppression, mountain pine beetle outbreaks, and white pine blister rust are compounding

factors in whitebark pine’s decline. Global climate change may increase the magnitude

of some threats, making it challenging to predict how whitebark ecosystems will respond

(Tomback and Achuff, 2010).

Fire is a natural occurrence in forest ecosystems. A burned area generates

openings in the forest, effectively setting back the “successional clock” (Tomback et al.,

2001). Clark’s nutcracker is known to select post-burn sites for seed caches (Tomback,

2001), which means that whitebark pine is an important species for forest regeneration

after a fire. New whitebark growth creates sheltered microsites for subalpine fir and

Engelmann spruce to grow, increasing conifer biodiversity along with forest regeneration

(Tomback and Resler, 2007).

Whitebark pine is less shade tolerant than the species that it shelters (Arno 1986).

Fire exclusion practices in the 20th

century have led to successional replacement of

whitebark pine by subalpine fir and Engelmann spruce (Arno 1986). This has changed

the structure of subalpine forests because conifer biodiversity is lost and the landscape

becomes homogenous. Many stands at a landscape level are now solely comprised of

late seral stage subalpine fir and Engelmann spruce (Keane, 2001).

Mountain pine beetles (Dendroctonus ponderosae) are native to western North

America. These beetles episodically attack large, mature pines with thick bark, resulting

in major outbreaks (Cole and Amman, 1969). They naturally occur in lodgepole pine

forests, but during outbreaks the insects spread to whitebark pine communities (Arno,

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1986). This can result in wide scale mortality. In the 20th

century, mountain pine beetle

outbreaks killed many mature whitebark pine trees in Idaho and Montana (Bartos and

Gibson, 1990; Jenkins et al., 2001).

Because fire suppression results in higher density and greater age of late

successional forests, this practice may increase the scale and abundance of mountain pine

beetle outbreaks (McGregor and Cole, 1985). Climatic warming facilitates pine beetle

population growth and may reduce whitebark pine defenses (Raffa et al., 2008).

Currently, mountain pine beetles are again in outbreak mode throughout the West, but at

a geographic scale considered unprecedented (Logan et al., 2010). This outbreak is

driven by milder winter temperatures (Logan and Powell 2001; Logan et al., 2010).

Whitebark pine stressed by competition from fire suppression may be even more

vulnerable to pine beetle attacks generated by warming trends (Logan et al., 2010; Raffa

et al., 2008).

A third cause of decline in whitebark pine is white pine blister rust, a disease

caused by the exotic pathogen Cronartium ribicola. This fungal pathogen, which infects

five-needle white pines of subgenus Strobus (McDonald and Hoff, 2001), was

inadvertently introduced to western North America in the early 1900’s through

importation of infected nursery seedlings from western Europe (Spaulding, 1909, 1911,

1922 as cited in McDonald and Hoff, 2001). Cronartium ribicola has evolved with

Eurasian pine species, which have resistance to this disease. Since its introduction to

North America, C. ribicola has exploited a range of host pine species with low natural

resistance (McDonald and Hoff, 2001) (Table I.1). Although many North American five-

needle pines are susceptible to infection by C. ribicola, they vary in susceptibility and

8

extent. Whitebark pine populations are currently being infected by C. ribicola nearly

range wide, with high infection levels in some areas; the resulting mortality is impacting

ecosystems throughout whitebark pine’s range (Tomback and Achuff, 2010).

Cronartium ribicola relies on both five- needled white pines and alternate hosts to

complete its life cycle (McDonald and Hoff, 2001). Ribes spp., the gooseberries and

currants, have long been recognized as alternate hosts, but recent research has discovered

that herbaceous plants in the genera Pedicularis and Castilleja may also act as hosts

(McDonald et al., 2006).

McDonald and Hoff (2001) describe the specific mechanism of white pine blister

rust transmission. Five-needle white pines are infected by C. ribicola when wind-blown

basidiospores from alternate hosts enter the stomata of pine needles. Rust mycelia grow

from the needle into the living wood of the pine tree and eventually produce a fruiting

canker, which leads to swellings on branches or stems of the tree (Figure II.2). The

canker sporulates, producing sacs of aeciospores, and these sacs ultimately burst through

the surface of seemingly healthy bark to release spores into the environment. Some

spores inevitably reach an alternate host and complete the cycle. Cankers eventually

girdle the branch or stem of the pine, cutting off the supply of water and nutrients. Since

seed cones are produced at branch tips, the accumulating dead branches reduce seed cone

production long before the tree itself dies.

Blister rust mortality is especially detrimental to whitebark pine, which requires

up to 50 years to reach reproductive maturity (McCaughey and Schmidt, 1990). Loss of

mature trees means a loss of cone production that potentially takes decades to replace.

However, seedlings, saplings, and smaller krummholz whitebark pine are also affected by

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C. ribicola, and die more rapidly from infection than their larger counterparts (Tomback

et al., 1995). This reduces the number of young trees available to regenerate the species.

Krummholz whitebark pines are also affected by white pine blister rust. It was

once thought that this pathogen could not survive the extreme winter temperatures of the

alpine treeline ecotone (Campbell and Antos, 2000), but high numbers of infected

individuals have recently been discovered among krummholz whitebark pine (Resler and

Tomback, 2008), suggesting that C. ribicola can reproduce and survive under the most

extreme conditions.

White pine blister rust may affect the keystone and foundational roles that

whitebark pine plays within the alpine treeline ecotone. Resler and Tomback (2008)

discovered that 33.7% of the whitebark pine in their sampled tree islands showed

evidence of white pine blister rust infection. This has serious implications because global

climate change is predicted to alter treeline dynamics (Tomback and Resler, 2007). If

whitebark pines in the alpine treeline ecotone are succumbing to blister rust at significant

levels, it may affect the way treeline is able to respond to climate warming and

potentially rising treeline elevations (Tomback and Resler, 2007; Resler and Tomback,

2008).

Global Climate Change and Treeline Impacts

Treeline forests are indicators of global climate change. These so called

‘bellwether’ ecosystems are often the first to show symptoms of stress (Smith et al.,

2009). Treeline is known to be dependent on several factors, including temperature, wind

speeds, nitrogen deposition, and concentration of carbon dioxide (Grace et al, 2002).

Treelines have responded to temperature fluctuations since the last glacial maximum

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(Lloyd and Graumlich, 1997). During the early Holocene, temperatures were 1.4° C

greater than the present, and treelines were on average 200 m higher than they are today

(Grace et al., 2002). Average temperatures are conservatively predicted to increase by

more than 2.5° C over the next century (Easterling, 2005). Warmer temperatures are

expected to cause an upward shift in treeline (Millar et al., 2004), with an estimated

elevation gain of 140 – 700 m (Grace et al., 2002).

Climate models have been generated to predict how whitebark pine will respond

to warming trends (Hamann and Wang, 2006; McKenney et al., 2007; Warwell et al.,

2007). These models are generally in agreement that whitebark pine will see a shift from

its current range. These models do not account for fine scale habitat features or

ecological processes, such as topography, soil nutrients, seed dispersal and germination,

or disturbance regimes (Loehle, 1996). However, they do provide a coarse estimate of

changes in range and habitat area. Some models show that while whitebark pine will lose

current distribution in the U.S., it will gain new habitat at higher latitudes and elevations.

Warwell et al. (2007) predict a 97% loss of suitable whitebark habitat in the U.S. by

2090. Hamann and Wang (2006) found that 73% of whitebark pine’s habitat will be lost

by 2085, but it should gain 76% of the original area at northern latitudes. McKenney et

al. (2007) predicted that by the end of the century, whitebark pine’s current range will be

reduced by 42%. However, whitebark pine will gain an expected 7.8% new habitat by

moving north approximately 6.4°. With global climate change shifting treeline to higher

latitudes and elevations, there is concern about whitebark pine’s ability to respond and

maintain its ecological roles due to stresses brought on by blister rust and other threats

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such as mountain pine beetle. If whitebark pine mortality is widespread, the ability of

treeline as a whole to move upwards could be compromised.

Clark’s nutcracker plays an important role in upward movement of whitebark pine

by caching seeds in the alpine tundra and treeline ecotone (Tomback, 1998; Tomback,

2001). Frequently, seeds are cached next to microsites such as rocks or ground

topography which potentially act as ‘nurse objects’, facilitating germination and

sheltering developing whitebark seedlings (Resler, 2004). Because whitebark pine then

in turn creates favorable microsites for less hardy conifers facilitating tree island

development, warming trends should result in krummholz tree islands shifting upwards in

elevation (Resler et al, 2005). With blister rust killing whitebark pine at treeline, the

number of favorable whitebark pine microsites available to subalpine fir or Engelmann

spruce is reduced. This may affect the ability of treeline to move upwards in the manner

predicted (Tomback and Resler, 2007; Resler and Tomback, 2008).

The more we can learn about whitebark pine’s ecological functions, the better we

can predict how ecosystems will respond to whitebark pine mortality. Presently, very

little is known about the mechanisms behind facilitation roles whitebark pine plays in the

alpine treeline ecotone, or how whitebark pine ecosystems will respond to increased

blister rust mortality and global warming trends. Our research will contribute to a better

understanding the role of whitebark pine in two study areas in Montana, facilitates tree

island initiation and maintenance within the alpine treeline ecotone.

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Figures and Tables

Figure I.1 Distribution of Pinus albicaulis in North America

(Tomback and Achuff, 2010)

13

Figure I.2 Image of active blister rust stem canker on an infected whitebark pine.

Light colored sections on stems are sacs containing aeciospores (Photo by Sarah

Blakeslee).

Table I.1 US and Canadian conifers susceptible to white pine blister rust infection

(McDonald and Hoff, 2001)

North American Tree Hosts

Whitebark Pine (Pinus albicaulis)

Foxtail Pine (Pinus balfouriana)

Rocky Mountain Bristlecone Pine

(Pinus aristata)

Great Basin Bristlecone Pine

(Pinus longaeva)

Southwestern white pine (Pinus strobiformis)

Limber Pine (Pinus flexilis)

Eastern White Pine (Pinus strobus)

Western White Pine (Pinus monticola)

Sugar Pine (Pinus lambertiana)

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CHAPTER II.

INTRODUCTION

Background

Certain plant species may act as keystone and foundational ecosystem

components by facilitating stability and biodiversity (Ellison et al., 2005). Research has

indicated the importance of facilitative plant interactions for survival and regeneration in

stressful environments (Lortie et al, 2004; Brooker et al, 2008). This is particularly true

for high elevation sites where abiotic stress is high (Callaway et al., 2002). Calloway et

al. (2002) examined 115 plant species in 11 mountain sites across the globe to determine

whether elevation, and thus environmental stress, changed plant community interactions.

They generally observed competitive interactions at lower elevations where

environmental conditions were moderate. At higher elevations, species interactions

predominantly switched to facilitation, whereby one competitor provided shelter for

another. Harsh environmental conditions may be moderated when facilitative species

provide a protective microclimate for germination and establishment, e.g., protection

from solar radiation or shelter from wind (Germino et. al., 2002; Baumeister and

Callaway, 2006).

As more examples of plant facilitation in high elevation communities are

discovered, it is increasingly apparent that facilitation is important to community

development in these extreme environments. For example, Cavieres et al. (2005, 2007)

observed plant interactions at the upper limit of vegetation in the Chilean Andes. Their

studies indicate that a cushion plant (Azorella monantha) moderates substrate and air

temperatures and enhances soil moisture and nutrients for both the native Andean

15

cauliflower (Nastanthus agglomerates) and the invasive field chickweed (Cerastium

arvense). Batllori et al. (2009) found that survival of Pinus uncinata seedlings planted in

the alpine treeline ecotone was increased when seedlings were located on the leeward

side of krummholz conifers, likely due to retention of sheltering snowpack in this

location during winter months.

The process of seedling establishment is important for long-lived plants such as

conifers. Years with successful seed germination are more frequent than years with both

high seed germination and high seedling survival (Cui and Smith, 1991). Seedling

establishment is particularly difficult in the alpine treeline ecotone because high winds,

variable temperatures, poorly developed soils, and intense solar radiation (Marr, 1977;

Arno and Hammerly, 1984; Finklin, 1986; Maher et al., 2005) make establishment a

challenge.

The likelihood of seedling survival is improved in the alpine treeline ecotone

when harsh climatic conditions are mitigated by rocks, topographic niches, and other

objects acting as protective microsites or “nurse objects”, providing windward shelter

(Germino, 2002; Resler, 2004; Batllori et al., 2009). Survival is further facilitated when a

solitary conifer establishes and other conifers grow in its lee, resulting in two or more

conifers growing together in close proximity as a multi-tree tree island. In the alpine

treeline ecotone, tree islands facilitate the survival of conifers species such as Engelmann

spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) (Resler and Tomback,

2008). Both species are less likely to be found on the windward rather than leeward side

of tree islands (Hattenschwiler and Smith, 1999), and Engelmann spruce seedlings have

16

been found to be associated with higher survival rates when windward or overhead

shelter, such as branches, is present (Germino et al., 2002).

In the cool, dry, and windy northerly eastern slope faces of the alpine treeline

ecotone on the eastern Rocky Mountain front, whitebark pine (Pinus albicaulis) is a

dominant ecosystem component (Smith et al., 2011; Resler and Tomback, 2008). This

species is dispersed by Clark’s nutcracker (Nucifraga columbiana) (Tomback, 1982), and

is tolerant of drought and high levels of solar radiation (Arno and Hammerly, 1984;

Maher et al., 2005; Tomback et al., 2001). Because whitebark pine often grows as a

solitary conifer (Maher et al, 2005; Resler and Tomback, 2008), it may act as a ‘nurse

tree’ by facilitating the survival of less hardy subalpine fir and Engelmann spruce on

harsh sites in the subalpine zone and in the alpine-treeline ecotone, where it facilitates

development of multi-tree tree islands (Callaway, 1998; Resler and Tomback, 2008).

Resler and Tomback (2008) found that within two study areas east of the Continental

Divide, 95.9% of multi-tree tree islands sampled included whitebark pine. Of these tree

islands, 48.5% had whitebark pine as the windward “initiator”, indicating the importance

of whitebark pine in facilitating the establishment of leeward conifers in this region.

Whitebark pine is presently designated a candidate for endangered species listing

by the U.S. Fish and Wildlife Service (USFWS, 2011). Fire suppression leading to

successional replacement by shade tolerant conifers, mountain pine beetle (Dendroctonus

ponderosae) outbreaks, and the disease white pine blister rust, caused by the invasive

fungal pathogen Cronartium ribicola, are the major factors in the decline of whitebark

pine. Within the alpine treeline ecotone, the most immediate threat to whitebark pine

populations is damage and mortality resulting from white pine blister rust. Infected small

17

diameter trees can progress from showing no outward disease symptoms to death within a

few years (Tomback et al., 1995). This rapid mortality may reduce the chances for

establishment of new tree islands, and also the health of existing tree islands by exposing

formerly leeward conifers to the wind.

Global climate change may increase the frequency and severity of fire regimes,

accelerate the rate and spread of pine beetle outbreaks, and likely alter the distribution

and infection rates of blister rust, making it challenging to predict how whitebark

ecosystems will respond (Tomback and Achuff, 2010). Warmer temperatures are also

expected to cause an upward shift in treeline (Millar et al., 2004), with an estimated

elevation gain of 140 – 700 m (Grace et al., 2002). As whitebark pine numbers decline,

this may impact the frequency of tree island establishment in the upper alpine treeline

ecotone, possibly altering the response of treeline as a whole to warming trends

(Tomback and Resler 2007).

Conceptual Framework

Although research has indicated that whitebark pine is an important component of

tree islands, very little is known about the specific mechanisms of facilitation leading to

tree island formation or about whitebark’s role in established tree islands. The overall

objectives for this study are to determine empirically and experimentally the attributes

and ecological interactions that enable whitebark pine to facilitate tree island

development, and to address how the mortality of whitebark pine from blister rust may

impact these ecosystem functions.

18

Hypotheses for Testing

This is an NSF-supported project with objectives already articulated. For my

master’s project I clarified goals and hypotheses, designed experiments, developed field

protocols, and had oversight responsibilitiy for a series of experiments and empirical

studies. I had the support of Dr. Diana F. Tomback and other students in the lab and

field, so often refer to the work as “we.” I am testing three separate hypotheses that

together support the overall research objective in a logical sequence (Figure II.1). My

hypotheses are as follows: 1) Whitebark pine is hardier than other alpine treeline ecotone

conifer species, as demonstrated by more vigorous growth and higher survival at treeline;

2) whitebark pine provides a more favorable microsite for tree island recruitment than

other common alpine treeline ecotone microsites; and 3) death of windward whitebark

pine in established tree islands leads to loss of vigor in leeward conifers.

The first hypothesis addresses whether there is differential growth vigor, if any, of

whitebark pine in comparison to other treeline conifers. Because tree islands usually

“migrate” leeward, the most windward tree is often the oldest (Holtmeier and Broll,

1992). The dominant presence of whitebark pine in this position suggests that this

species is hardy and serves an important role in recruiting tree islands through subsequent

facilitation by mitigating conditions for a leeward conifer. In order to establish a tree

island, whitebark pine seedlings must first become established in the harsh, and exposed

areas within the alpine treeline ecotone and then survive these conditions. This first

hypothesis is addressed through an empirical study, the “Relative Vigor Study” that

identifies and compares differences in survival and vigor among small krummholz

whitebark pine and the other two dominant treeline conifer species, subalpine fir and

19

Engelmann spruce. In Figure II-1, the importance of the initial establishment and

survival of whitebark pine to subsequent facilitation is illustrated in a visual model.

The second hypothesis states that because whitebark pine often facilitates tree

island development, it may provide more favorable growing conditions for leeward

conifers than other common treeline microsites. An alternative to this hypothesis is that

whitebark pine trees are simply more numerous at treeline, and this means that there are

more opportunities for tree islands to form in whitebark pine microsites. In the “Planting

and Sowing Study”, we test whether whitebark microsites are associated with higher

conifer germination and/or seedling survival rates than other common treeline microsites

in order to determine whether conditions are in fact more favorable. This would imply

that whitebark pine may offer facilitation or a higher quality of facilitation than other

microsites. The other alpine treeline ecotone microsites investigated include rocks,

another conifer – Engelmann spruce for consistency – and exposed sites with no apparent

shelter. In this study, we planted conifer seeds and seedlings leeward of four microsite

types and compared seed germination and seedling survival rates. This mechanism for

tree island development is represented in the #2 position of Figure II-1.

The last hypothesis directly tests the facilitation function of whitebark pine in

established tree islands. Since whitebark pine is often the intiating or most windward

conifer within a muilti-tree island, it may provide important leeward shelter to other

conifer species, mitigating the harsh wind and particle-blast of treeline environments

(Habeck, 1969; Resler, 2004). Blister rust is currently infecting and killing many

whitebark pine in some regions. We hypothesize that the loss of these windward

whitebark pines will result in exposure and thus damage to leeward conifers. As

20

indicated in the #3 position of Figure II.1, the “Girdling Study” simulates the effects of

blister rust on windward whitebark pine, and we monitored the growth and vigor of the

non-whitebark conifer immediately leeward.

21

Figures and Tables

Figure II.1 Overall conceptual model

The role of whitebark pine in tree island formation can be explained by 1, the

establishment of a solitary conifer in an exposed area without shelter from other tree

islands. To accomplish this, the species must be hardy and vigorous to withstand the

harsh treeline climate. We test this hypothesis in the Relative Vigor Study. As the

conifer establishes and grows, it generates a sheltering leeward microsite (indicated by

star), 2, where other conifers can germinate, eventually leading to the formation of a tree

island. We examine this hypothesized process by testing whether whitebark pine

microsites are associated with the highest conifer germination and survival rates in the

Planting and Sowing Study. Blister rust is currently killing whitebark pine at treeline,

potentially exposing leeward conifers to harsh wind and ice particles. The impact of this

windward shelter loss, 3, on established tree islands is unknown. We simulate blister rust

on windward whitebark pine (x indicates blister rust simulation) in the Girdling Study

and monitor impacts on the newly exposed leeward conifer.

22

CHAPTER III.

METHODS

Study Areas

This research was conducted over three field seasons within a bioclimatically

induced krummholz treeline at two separate study areas (Figure III.1). These study areas

were selected because whitebark pine is a major ecosystem component and also because

of the accessibility of treeline. The northern study area includes Divide and Whitecalf

Mountains, Montana. Divide Mountain is located on both Blackfeet Tribal Land, as well

as on the east slope (Rocky Mountain eastern front) of Glacier National Park at

approximately 48⁰ 39' 25" N and 113⁰ 23' 45" W. Treeline occurs at approximately 2200

m elevation. Whitecalf Mountain is located within the east slope of Glacier National

Park at 48⁰ 38' 20" N and 113⁰ 24' 08" W. Treeline occurs at approximately 2100 m

elevation. Divide and Whitecalf Mountains are characterized by steep slopes ( = 25.7⁰)

and poorly developed soils with limestone bedrock. Mountain avens (Dryas octopetala)

and bearberry (Arctostaphylos uva-ursi) are the dominant herbaceous understory

vegetation in this study area. Willows (Salix spp.) and junipers (Juniperus spp.) are also

distributed in patches. Subalpine fir, Engelmann spruce, and whitebark pine are the

dominant trees on Divide Mountain, but there is a noticeable absence of Engelmann

spruce on Whitecalf Mountain. Found in small numbers are limber pine (Pinus flexilis),

lodgepole pine (Pinus contorta), and Douglas-fir (Pseudotsuga menziesii).

The southern study area is located 530 kilometers directly southeast of the

northern study area on the Beartooth Plateau’s Line Creek Research Natural Area (RNA),

23

MT, in Custer National Forest at 45⁰ 01' 47.45" N and 109⁰ 24' 09.22" W. Subalpine fir

is nearly absent from the ecosystem and is only commonly found in the shelter of willow

patches or within dense tree islands. Engelmann spruce and whitebark pine are the two

most common species. Lodgepole and limber pine are found in very small numbers.

Krummholz rapidly grades into erect trees on the northeast-facing slope. Like the Divide

Mountain and White Calf study areas, the Line Creek RNA is characterized by many

solitary krummholz whitebark pine. The terrain is largely open with gentle slopes (~20°).

Sedges (Carex spp.), mountain meadow cinquefoil (Potentilla diversifolia.), American

bistort (Polygonum bistortoides), and silvery lupine (Lupinus argenteus) are common

among the herbaceous groundcover.

Relative Vigor Study

Two studies were conducted to examine relative vigor in different ways. One was

an observational study addressing our first hypothesis by comparing shoot lengths, shoot

growth rates, and vigor of the three most common treeline species in our study areas:

whitebark pine, subalpine fir, and Engelmann spruce. For the first study, we used

measurements representing readily obtainable characteristics of growth and vigor. We

originally intended to use seedlings for each species, but when selecting trees for this

study, we found very few seedlings at either study area. Instead, we selected small

krummholz trees that were already established. These trees ranged roughly from 1 to 40

years of age, based on stem constriction counts (Appendix I, Tables 1 and 2). These

small trees were unlikely to die during the short-term three year observation period. Our

measurements act as a surrogate for survival and are also important clues to any species

differences in ability to utilize resources from the environment. Shoot lengths are

24

particularly relevant to vigor and survival. Ability to increase photosynthetic canopy

rapidly is important at treeline, where growing seasons many only last a few months in

the summer. The second study was a random sampling effort to determine the relative

numbers of solitary krummholz trees of each species in our study area.

Field Methods. These studies were conducted both on Divide Mountain and at

the Line Creek RNA. In July 2010, 100 small solitary krummholz conifers (<30 cm

high) of the three major conifer species were haphazardly selected based on exposed

growing conditions, i.e., unsheltered by a tree island or other large microsites (Table

III.2). We marked conifers with a tagged leeward nailspike and monitored them from

July 2010 to September 2012. Stem diameter at ground level, length from 3-5 shoots

(defined as the total length of the new branch elongation plus extending needles), canopy

area (calculated using longest dimension and the dimension immediately perpendicular to

longest dimension), and woody tissue height were measured annually in July. The only

exceptions were shoot length measurements. In 2011 and 2012 we measured five shoots,

if available, from each tree, first in July near the beginning of the growing season and

then again in September after shoots were fully extended. The difference between the

July and September mean shoot length per tree was used to calculate the shoot growth

rates with the following formula: (September Mean Shoot – July Mean Shoot)/No. Days

between Measurements for each conifer (mm/day). Canopy areas were calculated using

the formula for the area of an ellipse (π × a × b).

All tree geographic locations were marked with a Trimble Geo XT handheld GPS

unit (GeoExplorer® 2008 series). Measuring tapes were used to measure height and

canopy area to the nearest half centimeter. Mitutoyo (500-195-20) digital calipers with a

25

precision of a hundredth of a millimeter were used to measure shoot lengths. Care was

taken to ensure consistency of remeasurement from year to year. In 2011, we marked

five branches on each conifer with zip ties so that the same terminal branch shoots were

remeasured. Each conifer stem was marked with tree specific paint to ensure consistency

in caliper placement for stem diameter measurements.

The shoot lengths of subalpine forest conifers, which are taller trees with large

diameters and upright growth forms, were measured for comparison with krummholz

conifers to see how shoots growing in less harsh conditions might differ. In both study

areas, larger stature trees occurred in sheltered sites and at the lower limit of the alpine

treeline ecotone. In 2011, five shoots each of 10 haphazardly selected conifers of each

species at each study area were measured in September. In 2012, we increased the

sample size to 20 conifers of each species at each study area. The measurement

procedure was identical to that of the small krummholz trees, except tree branches were

not marked, so the same trees were not necessarily revisited from year to year.

In order to determine the relative abundance and density of solitary krummholz

whitebark pine, subalpine fir, and Engelmann spruce, growing at treeline, transects 50 m

long transects with 10 m wide belts (500 m2) were established using a subset of randomly

generated GIS points within each study areas. We sampled twenty transects at each study

area. On each transect, solitary krummholz conifers growing in largely unsheltered

conditions were measured for species, height, canopy area, microsite, and qualitative

vigor. Qualitative vigor was determined using a four category ranking scale of poor to

excellent based on characteristics of windward needle death, health of new annual shoots,

and needle color (Table III.1).

26

Data Analysis. All data analyses were completed using R (GUI statistical

software program version 2.11.1). We compared krummholz shoot lengths among

species using a Kruskal-Wallis rank sum test (because data were not normally distributed

and sample sizes were unequal). Because of an unequal number of shoots per tree

resulting from small tree size and shoot death on some marked branches, one shoot per

tree was selected for this analysis using randomly generated numbers. Wilcox-signed

rank post-hoc tests with Bonferroni corrections were used for paired comparisons of

shoot lengths between species (α = 0.05).

Our shoot measurements do not separately account for branch extension length

and needle length, but rather the sum of both. We were interested in seeing if species

differences remained when needle length was subtracted from our measured shoot

lengths. This would compensate for any length attributed to the needle extending beyond

the branch extension point. We obtained minimum needle lengths for each of the three

species from Flora of North America (eFloras, 2008) and subtracted them from the mean

shoot lengths of the upright upper subalpine conifers, as follows: Engelmann spruce – 16

mm, subalpine fir – 18 mm, and whitebark pine – 30 mm. The reduced shoot lengths

were then compared by species in a series of Wilcox-signed rank tests with Bonferroni

corrections (α = 0.05) by year and study area. Nonparametric statistics were used

because data were not normallydistributed. Krummholz conifer shoots were not assessed

in this manner because their needle lengths were generally shorter than the minimum

needle lengths suggested by Flora of North America. This analysis would have resulted

in negative shoot lengths for krummholz individuals.

27

Other measurements analyzed with Kruskal-Wallis rank sum tests and Wilcoxon-

signed Rank post hoc tests with Bonferroni corrections (α = 0.05) were krummholz shoot

growth rates, krummholz canopy area, krummholz height, krummholz stem diameter, and

qualitative vigor of trees sampled in the transects. Non-parametric statistics were used

because of unequal sample sizes and data that were not normally distributed.

In order to use the mean shoot for each individual upright tree, we verified that

the within tree variation in shoot lengths was smaller than among species variation as

follows: The variation of the five measured upright subalpine conifer shoots was

compared within and among species with a Nested ANOVA. Mean shoot length was

calculated for each tree and compared with a One-way ANOVA and Tukey’s post hoc

test to determine species differences. Parametric statistics were used because data were

normally distributed and sample sizes were equal. Significance level was set at α = 0.05.

We used a Chi-Square test (α = 0.05) to analyze differences in microsites

responsible for establishment for the conifers sampled within the transects. In this

comparison there were two categories for microsite: the proportions of conifers found in

relatively unsheltered, unknown, or small microtopographic ground depression microsites

were compared to the proportions of conifers found established leeward of more

substantial shelter, such as rocks or vegetation. These proportions were also compared

between species at each study area.

We used a Binomial Distribution test (α = 0.05) to determine the proportional

occurrence of whitebark pine in relation to the number of all solitary conifers sampled for

the transects at each study area. This test was performed for each of the 20 transects at

each study area. The theoretical probability was considered to be an equal proportion of

28

each species (0.33). We determined how many of the 20 transects at each study area had

solitary whitebark pine in a significantly greater proportion than expected. The mean

density of each species was also calculated as the number of conifers per square meter for

each transect. These densities were compared visually in a figure.

Planting and Sowing Study

This study addresses hypothesis 2 by comparing the survival rates of planted seedlings

and germination and survival rates of sown seeds leeward of four common treeline

microsites.

Field Methods. Study areas were Divide Mountain and the Line Creek RNA.

For the Divide Mountain seed sowing and seedling planting study, we collected

Engelmann spruce and subalpine fir cones at Divide Mountain in September, 2010. The

Colorado State Forest Service Nursery found that the Engelmann spruce cones contained

non-viable seeds, so only subalpine fir seeds were available for the study. The quantity

of subalpine fir seeds was adequate only to produce enough seedlings for the seedling

planting component of the study. Engelmann spruce seeds from the same seed transfer

zone (McDonald Pass, Helena National Forest, 6300 m elevation) were provided by the

USDA Forest Service Nursery in Coeur d'Alene, ID.

On the entire Beartooth Plateau there was no cone crop for either spruce or fir in

2010. Engelmann spruce seeds collected by the Dubois Ranger District of the Shoshone

National Forest were provided by the USDA Forest Service Nursery in Bessey, South

Dakota. These seeds were collected at an elevation of 2712 m, and were the highest

elevation Engelmann spruce seeds available in the same seed transfer zone as the

29

Beartooth RNA. These seeds were used for both the direct seed sowing experiment and

the seedling planting experiments.

Seeds for the direct sowing component of the study were chilled for 4 months at

approximately 35° before planting. Seedlings were grown by the Colorado State Forest

Service Nursery in Fort Collins, CO.

Planting and sowing took place in July 2011. In each study area, we located 20

replicates each of the four microsites – krummholz whitebark pine, krummholz spruce,

rock, and exposed site – for the seedling planting study, and at an additional 20 replicates

of each microsite for the seed sowing study. Microsite geographic locations were marked

with a Trimble Geo XT handheld GPS unit (GeoExplorer® 2008 series). Microsites

ranged in height from 4 – 66 cm. In general, conifer microsites were taller than rocks.

For each study, spruce and whitebark microsites were selected based on similar heights

(Appendix III). Once suitable microsites were marked with a numbered nailspike, either

5 seeds or 2 seedlings were placed immediately leeward of the microsite object or in the

middle of the exposed microsite. Leeward direction was determined by observing

dominant flagging of krummholz conifers. Seedlings were labeled with colored zip ties

placed at the base of the stems for 2012 identification and planted in 25 cm holes dug to

fit the container substrate (Figure III.2). Seeds were planted 0.5 cm deep. Seedling sites

received 1 liter of water at the time of planting, and seed sites received ½ liter of water at

the time of planting. Germination and survival were assessed in July 2012. At this time,

terminal shoot lengths were measured and qualitative vigor on a poor to excellent four

level categorical scale assessed for surviving seedlings. Germinated seeds were located

30

and counted by microsite type (Figure III.3). Germination sites were revisited September

2012 to document germinant survival over the summer months.

Data Analysis. The results of this study were analyzed separately by planting

type, microsite type, and study area. Data that were not normally distributed were

analyzed with non-parametric statistics. Significance levels were set at α = 0.05. The

resulting analyses included one-year seedling survival rates and seedling vigor

assessments, and seed germination and new seedling summer survival for both Divide

Mountain and Line Creek RNA.

One year seedling survival was examined with a Pearson’s Chi-squared test of

independence to assess whether survival differed among microsite types. Qualitative

vigor was also analyzed with a Pearson’s Chi-squared test to determine differences

among microsite types. Terminal shoot lengths were compared among microsite types

with a Kruskal-Wallis rank sum test.

July, 2012, seed germination among microsite types was compared with a

Fisher’s Exact probability test (α = 0.05). September, 2012 summer survival numbers of

these initial germinants were also analyzed with a Fisher’s Exact probability test to

examine survival associated with the different microsite types. If any Fisher’s e act test

results were statistically significant, we compared the observed survival values to Chi-

square expected survival values to determine which microsite type(s) contributed the

most towards statistical significance. Lastly, the relative risk of survival or death by

microsite type was calculated using an odds ration calculation comparing actual

germinant summer survival numbers to expected values. A ratio near 1.0 was interpreted

as relative risk of survival or death near expected.

31

Girdling Study

This study addresses the third hypothesis by simulating whitebark pine mortality

caused by blister rust infection.

Field Methods. The girdling study was conducted on both Divide and Whitecalf

Mountains. In July 2010, we selected tree islands with whitebark pine as the windward

species, and placed them into either control or experimental groups. In order for a site to

be classified as experimental, the windward whitebark tree had to be infected with blister

rust. This was a condition of the research permits issued by both Glacier National Park

and the Blackfeet Indian Reservation. There were a total of 44 sites in this study, with an

equal number of control and experimental sites. All site locations were marked with a

Trimble Geo XT handheld GPS unit (GeoExplorer® 2008 series).

At each site, the conifer species immediately leeward of the whitebark pine was in

most cases subalpine fir (n = 40) with the remaining sites Engelmann spruce. We

collected baseline measurements of height, vigor, shoot lengths, and canopy on both the

windward whitebark pine and the immediately leeward conifer. Heights were measured

to the nearest half centimeter with a metric tape measure. Shoot lengths were measured

using Mitutoyo (500-195-20) digital calipers with a hundredth of a millimeter precision.

After baseline measurements were collected, the whitebark pine at all experimental sites

was defoliated and girdled (Figure III.4). This was accomplished by manually removing

all foliage from the tree and sawing deep grooves completely around the trunk to ensure

no future growth. This simulated the effects of blister rust infection, which reduces small

trees to branch and stem skeletons in a short time period (Tomback et al., 1995). In sites

that had extensive layering and tree islands larger than the windward whitebark pine, only

32

the discrete section which was sheltered by the whitebark pine was used for

measurements.

In 2011 and 2012, we monitored the effects of girdling on the conifer

immediately leeward of the whitebark pine. Subsequent measurements were taken to

determine whether exposure on the experimental sites impacted the leeward conifer

measurements of length of new shoots, shoot mortality, and qualitative tree vigor (Table

III.1), differently than control sites. In July 2011, zip-ties were placed on the branches

immediately leeward of the whitebark in both the control and experimental sites for

repeated measurements. At this time, we also marked shoots from fully wind exposed

subalpine fir or Engelmann spruce conifers in the same tree island that were not

associated with our experimental or control conifer (Figure III.5). These trees represent a

natural measure of exposure to windward conditions in the same tree island and are of the

same leeward species we are investigating.

Data Analysis. The difference in 2010 and 2012 vigor of the leeward conifer was

determined and categorically ranked as follows: loss of vigor, no change in vigor, or

increase in vigor. Categories were compared between control and experimental site

leeward conifers with a Fisher’s E act Probability Test. If this test was statistically

significant, the categories contributing most to significance were determined using

observed values vs. Chi-square expected values.

The changes in leeward conifer shoot lengths over the 2010 – 2012 time period

were compared in a Before-After-Control-Impact (BACI) analysis. One measured shoot

length per leeward conifer per year was randomly selected for comparison between

control and experimental site groups.

33

We also compared shoot length and mortality by treatment type at a tree island

level. We paired the natural, wind exposed shoots to the shoots experimentally exposed

by girdling or sheltered by a control whitebark from the same tree island. We determined

differences between these pairings for 2011 and 2012 shoot lengths and also the

proportion of dead shoots over the 2011-2012 timeframe. One shoot was randomly

selected from the five sampled per tree. The shoot length differences were compared in a

BACI analysis, and shoot mortality differences were examined with a Wilcox-Signed

rank test with a Bonferroni correction to determine whether experimental leeward

conifers had shorter shoot lengths and higher shoot mortality than control conifers.

34

Figures and Tables

Figure III.1 Research study areas

Divide and Whitecalf Mountains, MT, in east Glacier NP and Blackfeet Reservation

(48° 39' 25" N, 113⁰ 23' 45” W), and Line Creek Research Natural Area, Custer National

Forest, MT (113⁰ 01' 47” N, 109° 24' 09" W). Basemap from Montana Government

Natural Resources: http://nris.mt.gov/gis/gisdatalib/mtmaps.aspx.

35

Table III.1 Qualitative vigor categories

This table demonstrates the characteristics responsible for classifying conifers into a

particular vigor category. A conifer is assigned to a category based on meeting the

majority of specified criteria.

Excellent Good Fair Poor

Windward

Appearance

Tree may be

flagged, but no

obvious windward

damage

Minimal windward

damage, but only on

a few branches

Most windward

branches are

damaged to some

extent

Tree is

extensively

flagged with

lots of

windward

die-off

Needle

Health and

Color

Needles are long &

numerous; color is

characteristic of a

healthy specimen

by species (i.e.,

dark blue green for

whitebark)

Needles generally

healthy, but may

have slightly

yellowish color due

to drought

conditions

Some needles have

been blasted

and/or yellow due

to drought

Red or brown

dying and

dead needles

are numerous

New Shoot

Status

Numerous new

shoots throughout

entire tree; shoots

are fully developing

and healthy

Many shoots

present, but some

may be

underdeveloped

New shoots

developed, but

were blasted

and/or are

underdeveloped

New shoots

generally

absent from

branch tips

Table III.2 Sample sizes of small krummholz trees in the relative vigor study

Whitebark Pine Subalpine Fir Engelmann Spruce

Divide Mountain 17 15 15

Line Creek RNA 21 12 20

36

Figure III.2 Planted seedlings at the Line Creek RNA

Nursery grown Engelmann spruce seedlings planted on the leeward side of the four

experimental microsites at the Line Creek RNA. Microsites are as follows: a. whitebark

pine, b. Engelmann spruce, c. rock, and d. open or unprotected. These images were taken

in July 2011 at the time of planting. (Photo credits Sarah Blakeslee)

a.

b.

c.

d.

37

Figure III.3 Germinated seeds on Divide Mountain

Representative Engelmann spruce seedling cluster on Divide Mountain showing

germination in three out of five sown seeds; this image was taken in July 2012 shortly

after germination and early in the treeline growing season. (Photo credit Sarah Blakeslee)

38

a.

b.

Figure III.4 Example of before and after girdling and defoliation treatment

Representative girdling treatment site. In image a, the whitebark pine is sheltering

the windward edge of the tree island. Image b shows the subsequent exposure after

sawing through the main stem and defoliating the tree. Leeward conifer measurements

were taken in the areas previously sheltered by the whitebark pine. (Photo Credit: Sarah

Blakeslee)

39

Figure III.5 Leeward shoot vs. exposed shoot sampling areas

This image shows representative sampling locations for leeward shoot and exposed

shoot measurements in the girdling study. The leeward shoots were measured on the

subalpine fir immediately leeward of the whitebark pine. Exposed shoots were measured

on a conifer of the same species located elsewhere on the same tree island but without

windward whitebark protection. The exposed shoots represent natural exposure and

serve as a baseline comparison to the initially protected leeward shoots. This procedure

was done for control as well as experimental tree islands. (Photo Credit: Sarah Blakeslee)

40

CHAPTER IV.

RESULTS

Relative Vigor Study

Transects. On Divide Mountain, 487 solitary, wind-exposed (unsheltered)

krummholz conifers were sampled within 20 transects. Species composition comprised

64% whitebark pine (n = 312), 23% subalpine fir (n = 111), and 13% Engelmann spruce

(n = 64). At the Line Creek RNA, 209 solitary exposed krummholz conifers were

sampled. We found species composition to be 83% whitebark pine (n = 174), 15%

Engelmann spruce (n = 32), and 1.4% subalpine fir (n = 3). Binomial tests of individual

transects indicated that sampled whitebark pine was present at statistically significant

higher abundances than expected at both Divide Mountain (79% transects with solitary

trees, n = 15/19) and the Line Creek RNA (80% transects with solitary trees, n = 12/15)

(Table IV.1). Transects with no solitary trees present were not included in these

analyses.

Based on transect data for solitary trees, tree density per square meter was

calculated by species for each study area (Figure IV.1). Divide Mountain had greater

densities for all three species than Line Creek RNA. At both study areas whitebark

densities were the highest of the three species (Divide = 0.031 ± 0.03 trees/m2; Line

Creek RNA = 0.017 ± 0.02 trees/m2). Whitebark pine densities with respect to other

species were as follows: Divide Mountain – 5 times spruce and 3 times fir; Line Creek

RNA – 5.5 times spruce and 58 times fir.

41

Differences in qualitative vigor trends between species were found on Divide

Mountain (Kruskal-Wallis rank sum χ2 = 18.9, df = 2, P = 7.8e-5). Whitebark pine had

higher vigor than both fir (W = 19557, P = 0.037) and spruce (W = 13026.5, P = 2.08e-

5). Fir vigor was higher than spruce (W = 2981.5, P = 0.047). Statistical differences in

species vigor were not observed at the Line Creek RNA (Kruskal-Wallis rank sum χ2 =

0.82, df = 2, P = 0.67).

Trends in microsites associated with initial tree establishment were characterized

by species. On Divide Mountain we found a statistical difference among proportions of

species found in unknown or minimally protecting microsites (i.e., small ground terraces)

compared to those leeward of more sheltering rocks or vegetation (χ2 = 9.769, df = 2, P =

0.008). This difference was due to a larger than expected number of whitebark pine that

established with no clear protective microsite or minimal protection and a greater than

expected number of subalpine fir associated with more sheltering microsites. At the Line

Creek RNA, similar statistical differences were also found (χ2 = 11.3217, df = 2, P =

0.003). Statistical significance largely derived from a proportionally greater than

expected number of spruce and fir in more sheltering microsites.

Shoot Lengths and Shoot Growth Rate Comparisons. Krummholz shoot

lengths were compared by year between the species. Trends in length were similar

regardless of year and study area. Krummholz whitebark pine shoots were roughly 2 to 3

times longer than both spruce and fir shoots; and spruce and fir shoot lengths were not

statistically different from each other (Table IV.2).

In nearly all comparisons across study areas, whitebark pine’s shoot growth rates

from July to September were the highest of all comparisons, representing growth rates on

42

average 2 to 10 times faster than fir and spruce. Fir and spruce growth rates were not

statistically different (Table IV.3). The one exception to this trend was similar growth

rates for subalpine fir and whitebark pine on the Line Creek RNA in 2011.

For upright, subalpine conifers, in all comparisons of shoot lengths by year and

study area, nested ANOVA results demonstrated that within tree variance contributed

only minimally to the overall variance (0.25% +/- 0.19). The largest portion of variance

was described by differences between means of different species (97.6% +/-1.98%).

Because within tree variance was low, we used the mean of the five measured shoots per

individual tree for all upright shoot comparisons for all subsequent analyses. Similar

trends were observed at both study areas (Table IV.4). On Divide Mountain, the same

shoot length trends occurred in both years: whitebark shoots were 1.5 to 3 times longer

than both spruce and fir shoots. In 2011, spruce and fir shoots were not statistically

different, but in 2012 spruce shoots were longer than fir shoots. At the Line Creek RNA,

the same trend was observed in both years: whitebark pine shoots were 2 to 3 times

longer than both fir and spruce shoots, and the latter two species were not statistically

different.

Means of upright conifer shoot lengths after the minimum needle length was

subtracted were also compared in a One-Way ANOVA with a Tukey’s post hoc by year

and study area to determine if species differences still existed. One-Way ANOVA

analyses were significant (Divide, 2011: F = 7.2835, df = 2, P = 0.003, 2012: F = 54.3, df

= 2, P = 6.297e-14; Line Creek, 2011: F = 10.861, df = 2, P = 0.0003, 2012: F = 197.23,

df = 2, P < 2.2e-16). Tukey’s Post Hoc tests revealed that species mean shoot length

trends to remain mostly unchanged in most comparisons (Table IV.5). Whitebark pine

43

shoots were longer than subalpine fir shoots on Divide and Line Creek RNA. Whitebark

pine shoots were not longer than Engelmann spruce shoots on Divide in 2011, but they

were longer in 2012. At the Line Creek RNA, whitebark pine shoots were longer than

Engelmann spruce shoots in both years. Subalpine fir shoots were found to be equal in

length to Engelmann spruce in all comparisons.

With only one exception, upright shoots were longer than krummholz shoots of

the same species at each study area. The one exception was subalpine fir shoots on the

Line Creek RNA in 2012, where krummholz shoots did not differ from the upright shoot

counterparts. Proportions of upright to krummholz shoots were generally similar

between species for both years at both study areas (Table IV.6).

Other Small Tree Measurements. 2010 – 2012 increases in small tree stem

diameter, canopy area, and height measurements did not produce consistent statistical

differences in terms of species trends. Kruskal-Wallis rank sum test for differences in

median stem diameter resulted in no statistical differences among species on Divide

Mountain (χ2 = 0.83, df = 2, P = 0.66). At the Line Creek RNA, this test was significant

(χ2 = 11.9, df = 2, P = 0.003). Median whitebark pine stem diameter increases were

smaller than both spruce (W = 294.5, P = 0.03) and fir (W = 34, P = 6.1e-4). Spruce and

fir median stem diameter increases were not significantly different from each other (W =

92.5, P = 0.29) (Appendix II, Table 1).

Kruskal-Wallis rank sum analysis of median canopy area increase indicated no

statistical differences among species at the Line Creek RNA (χ2 = 12.029, df = 2, P =

0.21). On Divide Mountain, whitebark had a greater median increase in canopy area than

Engelmann spruce (W = 140, P = 0.02), but not subalpine fir (W = 103, P = 0.95).

44

Subalpine fir and Engelmann spruce were not statistically different (W = 20, P = 3.6e-4)

(Appendix II, Table 2).

Analyses of median height increases resulted in no significant differences at either

study area (Appendix II, Table 3).

Results Summary. We observed higher proportions of whitebark pine growing

in a solitary exposed state as compared to subalpine fir and Engelmann spruce.

Whitebark pine was also more common in minimally sheltering microsites than other

species. These results indicate that whitebark pine may be able to survive in harsh

treeline conditions better than spruce and fir.

Proportions of krummholz shoot lengths to upright tree shoot lengths were similar

between species, with krummholz shoot lengths being generally shorter than upright tree

shoot lengths. This indicates that there is a reduced ability to grow at treeline for all three

species. In terms of growth, whitebark pine produced the longest shoot lengths, both as a

krummholz and upright tree. Whitebark shoot growth rates were also generally faster

than spruce and fir. These results indicate the whitebark pine is capable of vigorous

growth during short growing seasons.

Planting and Sowing Study

Seedling Survival. One year after planting on Divide Mountain, the nursery

grown subalpine fir seedlings experienced very high overall mortality (90%). Of the 40

total seedlings planted per microsite type, survival was as follows: whitebark microsites –

12.5% (n = 5), spruce microsites – 7.5% (n = 3), rock microsites – 5% (n=2), and open

microsites – 15% (n=6). Chi-square goodness of fit analysis showed no significant

differences in survival among microsite types (χ2 = 2.5, df = 3, P = 0.47).

45

Survival of the planted Engelmann spruce seedlings was generally higher at the

Line Creek RNA, but overall mortality was high at 63.1%. Of the 40 seedlings planted

per microsite type, survival was as follows: whitebark microsites – 32.5% (n=13), spruce

microsites – 35% (n=14), rock microsites – 42.5% (n=18), and open microsites – 37.5%

(n=14). There were no significant differences in survival among microsite types (χ2 =

0.59, df = 3, P = 0.9) (Figure IV.2). A One-Way ANOVA test for differences in mean

apical terminal shoot lengths of surviving seedlings did not show any statistical

differences among microsite type at either study area (Line Creek: F = 0.26, df = 3, P =

0.85; Divide: F = 2.3, df = 3, P = 0.81).

We found qualitative measurement of seedling vigor to differ statistically among

microsite types in Fisher’s E act Tests (Divide P = 7.3e-4; Line Creek P = 9.6e-3). On

Divide, whitebark microsites had a greater than expected number of seedlings classified

as “e cellent vigor”. Spruce and rock microsite vigor trends were distributed across

vigor classes as expected. Open microsites had a greater than expected number of poor

and fair vigor seedlings and no seedlings in the good and excellent vigor classes.

At the Line Creek RNA, whitebark pine microsites had a greater than expected

number of excellent vigor seedlings. Spruce microsites had fewer than expected good

vigor class seedlings and a greater than expected number of excellent vigor seedlings.

Vigor of rock microsite seedlings was distributed among classes as expected. Open

microsites had a greater than expected number of good vigor seedlings and a fewer than

expected number of excellent vigor seedlings.

46

Seed Germination and Summer Survival. Sown Engelmann spruce seed

germination totals were counted in July 2012, and revisited in September 2012 to assess

proportion of summer survival by microsite type.

Seed germination numbers at the Line Creek RNA were small. Of the 400 seeds

planted, only 7 (1.8%) germinated. The germinant distribution was as follows:

Whitebark microsites – 2, spruce microsites – 3, rock microsites – none, and open

microsites – 2. There were no significant differences in germination among the four

microsite types (Fisher’s E act Test, P = 0.44).

On Divide Mountain, 80 out of 400 (20%) seeds germinated. In July, whitebark

microsites had 12 germinants, spruce microsites had 17 germinants, rock microsites had

32 germinants, and open microsites had 19 germinants. Differences in germination

among the different microsite types was statistically significant (Fisher’s E act Test, P =

0.01). This is largely due to more germinations than expected in rock microsites and

fewer than expected in whitebark microsites.

When sites were revisited in September 2012, we observed that both study areas

had experienced substantial germinant mortality over the summer months. At the Line

Creek RNA, only 3 out of 7 germinants survived: 2 at whitebark microsites and 1 at a

spruce microsite. Rock and open microsites had no living germinants. Mortality was not

significantly different among microsite types (Fisher’s E act Test, P = 0.31).

On Divide Mountain, 42 out of 80 germinants survived. The numbers of

surviving germinants are as follows: whitebark microsites – 11 seedlings, spruce

microsites – 8 seedlings, rock microsites – 18 seedlings, and open microsites – 5

seedlings (Figure IV.3). Difference in microsite type survival was statistically significant

47

(Fisher’s E act Test, P = 0.004). A comparison of observed survival numbers vs. Chi-

square expected survival numbers revealed that whitebark microsites were associated

with a 5.7 times greater than expected survival advantage and a very low comparative

risk of death (0.18 times expected). Expected values were at or near 1.0. Open

microsites had the lowest chance of survival after germination at 0.64 times greater than

expected and were associated with a 1.56 times greater than expected relative expected

risk of death. Spruce and rock microsites had relative survival advantages very close to

expected (0.89 and 1.08 times expected, respectively). Similarly, these microsites had

relative death risks close to expected (1.12 and 0.93, respectively) (Table IV.7).

Results Summary. The seedling planting experiment did not have significant

results in terms of survival or terminal shoot lengths by microsite type. However, conifer

microsites were generally associated with higher overall health and vigor of the leeward

seedlings.

For the seed sowing experiment, only the results at the Divide Mountain study

area were instructive. Very few seeds germinated at the Line Creek RNA, and thus

differences among microsite types were not observed. On Divide Mountain, rock

microsites initially favored germination. However, whitebark pine microsites had the

highest seedling summer month survival, indicating better shelter and perhaps more

favorable growing conditions than the other microsites examined. Whitebark microsites

also had the highest summer survival at the Line Creek RNA, but small sample sizes did

not yield statistical significance.

48

Girdling Study

Three ungirdled control whitebark trees (1 on Divide Mountain and 2 on White

Calf) were infected by blister rust and died over the course of the study. Their death

resulted in canopy defoliation. As a result, they no longer provided windward shelter to

the leeward conifer. These three sites were removed from analyses.

Leeward Conifer Vigor. There was a statistically significant difference between

control and experimental groups in the change in leeward conifer vigor over the 2010 to

2012 time period (n = 44, Fisher’s Exact Test, P = 0.002). This difference was attributed

to the number of experimental sites that lost vigor (77%; n = 17 of 22), and the number of

control sites that remained the same or increased vigor over the course of the study (79%;

n = 15 of 19).

Shoot Lengths. A BACI analysis comparing leeward conifer shoot lengths for

experimental or control treatment type indicated statistical differences over the course of

the study for treatment in terms of year (F = 8.17, df = 2, P = 0.005). Initially in 2010,

there were no differences in shoot length by treatment site type (Wilcox Signed Rank, W

= 236, P = 0.89). Overall, shoot lengths generally decreased over time, with

experimental sites experiencing greater decline in length than control sites (Figure IV.4).

The greatest difference in shoot lengths occurred over the 2011 to 2012 time period,

where shoot mortality was also highest (Wilcox Signed Rank, W = 74.5, P = 0.0003). A

partial explanation for this decline in overall sample mean shoot lengths is the mortality

of some marked shoots over the course of the study.

We compared naturally exposed and leeward conifer shoot lengths for 2011 vs.

2012. We analyzed these shoots lengths by computing the difference between leeward

49

and exposed shoots for each tree island in the study. The differences were then compared

in a BACI analysis as groups of tree islands with a either control or girdled whitebark

pine for 2011 and 2012 (Figure III.4). Results did not show different trends of mean

shoot length changes in terms of treatment type from year to year (F = 1.4, df = 2, P =

0.24). However, there was a difference in shoot lengths based solely on treatment type.

Control and experimental tree island shoot length differences were significant (F = 26.2,

df = 1, P = 2.3e-6). There was a significantly greater difference in exposed vs. leeward

shoot lengths for tree islands with a control whitebark than a girdled whitebark: leeward

conifers in control tree islands had on average of 10 cm longer shoot length compared to

exposed shoots. In experimental tree islands, this difference was roughly 1 cm with

exposed shoots slightly longer than leeward shoots. This indicated that tree islands with

a dead windward whitebark pine will have reduced shoot length growth similar to areas

of the tree island with no windward protection. Results also indicated that the presence

of a windward whitebark pine is associated with longer annual shoot growth.

Shoot Mortality. The proportional differences in mortality from 2011 to 2012

between naturally exposed shoots and experimental or control leeward shoots from the

same tree island were statistically significant (W = 129.5, P = 0.05). There were similar

differences in mortality between naturally exposed shoots with no windward protection

and the shoots leeward of a girdled whitebark pine. Leeward conifers in control sites had

an overall lower proportion of mortality than experimental leeward conifers. Mean shoot

mortality was 18.9% (SE = 5%) for control leeward conifers and 59% (SE = 7.7%) for

experimental leeward conifers, indicating that presence of windward whitebark pine

reduced shoot mortality in leeward conifers.

50

Results Summary. After losing windward whitebark pine shelter, leeward

conifers in experimentally girdled sites lost health and vigor over the course of the study.

These conifers also experienced shorter shoot lengths and higher shoot mortality than

leeward conifers in control sites with shelter from a windward whitebark pine, which

supports the hypothesis that loss of a windward whitebark from blister rust will be lead to

decreased health of the immediately leeward conifer(s).

51

Figures and Tables

Figure IV.1 Solitary krummholz tree density by species on Divide Mountain and

Line Creek RNA

The number of conifers per square meter was calculated using the mean from 20

transects at each study area. We found that Divide Mountain had the highest solitary

krummholz conifer densities. Whitebark pine had the highest density at both study areas.

WB ES SF

Species

Co

nife

r D

en

sity (

# T

ree

s / m

2)

0.0

00

.01

0.0

20

.03

0.0

4

Divide

Line Creek

52

Table IV.1 Species abundances of solitary conifers in transects

The number of solitary krummholz conifers is shown by transect per study area.

P- values are the result of binomial distribution tests comparing whitebark pine to an

expected equal distribution of 33%. The total number of transects that had a higher than

expected number of solitary whitebark pine is shown at the bottom of the P – value

column. Bolded P – values are transects that had a higher abundance of solitary

whitebark pine than expected based on an equal distribution of the three species.

Divide Mountain

Transect ID # WB # SF # ES # Total P - Value

1 18 6 8 32 0.0037

2 26 6 6 38 6.71e-10

3 4 0 0 4 0.012

4 25 3 6 34 1.31e-6

5 39 10 19 68 2.073e-5

6 14 1 6 21 0.001

7 10 0 0 10 1.53e-5

8 15 0 0 15 5.99e-8

9 19 15 1 35 0.0048

10 3 16 4 23 0.02

11 3 0 0 3 0.036

12 0 0 0 0 n/a

13 2 8 1 11 0.16

14 17 9 0 26 0.006

15 22 5 1 28 8.71e-7

16 1 0 0 1 0.33

17 5 8 1 14 0.21

18 10 1 2 13 0.0013

19 75 23 9 107 3.95e-15

20 4 0 0 4 0.012

TOTAL: 312 111 64 487 15/19

Line Creek RNA

Transect ID # WB # SF # ES # Total P - Value

1 26 0 0 26 3.03e-13

2 3 0 0 3 0.036

3 1 0 0 1 0.33

4 11 0 0 11 5.05e-6

5 1 0 0 1 0.33

6 4 0 0 4 0.012

7 25 0 0 25 9.18e-13

8 6 1 1 8 0.02

9 49 8 0 57 1.71e-16

10 0 0 0 0 n/a

11 12 12 2 26 0.059

12 4 1 0 5 0.04

13 12 9 0 21 0.013

14 0 0 0 0 n/a

15 0 0 0 0 n/a

16 0 0 0 0 n/a

17 7 0 0 7 0.00043

18 8 1 0 9 0.00085

19 0 0 0 0 n/a

20 5 0 0 5 0.0039

TOTAL: 174 32 3 209 12/15

a.

b.

53

Table IV.2 Krummholz shoot lengths

2011 and 2012 shoot lengths (mm) were compared using Kruskal-Wallis Analysis

Rank Sum Test and Wilcox Post Hoc with a Bonferroni correction at a, Divide Mountain

and b, Line Creek RNA. All P – values and test statistics shown are from the Wilcox

Post Hoc.

Divide Mountain

Year Length

Spp., Mean (SE)

n Species

Comparisons

W -

Statistic

P -

Value

2011 WP 22.0 (2.93)

ES 8.8 (0.85)

SF 11.3 (1.56)

17

15

15

WB > ES

WB > SF

SF = ES

205

190

149

5.3e-4

0.006

0.14

2012 WP 28.7 (3.35)

ES 9.3 (0.92)

SF 11.0 (1.15)

17

15

15

WB > ES

WB > SF

ES = SF

349

192

82

6.1e-6

8.4e-5

0.36

Line Creek RNA

Year Length

Spp., Mean (SE)

n Species

Comparisons

W -

Statistic

P -

Value

2011 WP 48.1 (4.25)

ES 22.9 (2.45)

SF 27.6 (3.34)

21

12

20

WB > ES

WB > SF

SF = ES

36

36

149

2.8e-5

4.0e-4

0.36

2012 WP 70.16 (3.67)

ES 26.24 (3.74)

SF 23.99 (4.3)

21

12

20

WB > ES

WB > SF

ES = SF

16

220

90

1.0e-8

0.0004

0.35

a.

b.

54

Table IV.3 Krummholz tree shoot growth rates

2011 – 2012 shoot growth rates units are mm/day. Tukey’s post hoc test p-values

are shown for a, Divide Mountain and b, Line Creek RNA. All P – values and test

statistics shown are from the Tukey’s Post Hoc. Sample sizes are shown for trees that did

not display a negative growth rate (i.e., loss of shoot growth from wind blasts or

experimental error in caliper placement)

a.

b.

Line Creek RNA

Year Growth Rate

Spp., Mean (SE)

n

Species

Comparisons

t -

Statistic

df

P –

Value

2011 WP 0.52 (0.06)

ES 0.25 (0.41)

SF 0.42 (0.03)

20

20

10

WB > ES

WB = SF

SF > ES

9.22

1.57

-13.16

38

28

28

0.0021

0.80

0.069

2012 WP 0.21 (0.03)

ES 0.033 (0.01)

SF 0.022 (0.007)

21

20

11

WB > ES

WB > SF

ES = SF

4.93

5.37

0.87

39

30

29

6.1e-6

4.3e-5

0.96

Divide Mountain

Year Growth Rate

Spp., Mean (SE)

n

Species

Comparisons

t -

Statistic

df

P -

Value

2011 WP 0.21 (0.02)

ES 0.069 (0.01)

SF 0.093 (0.06)

15

11

15

WB > ES

WB > SF

SF = ES

5.50

4.04

-1.20

24

24

20

2.8e-5

3.8e-4

0.69

2012 WP 0.15 (0.03)

ES 0.03 (0.007)

SF 0.05 (0.007)

17

15

15

WB > ES

WB > SF

ES = SF

4.59

3.88

-2.01

30

30

28

1.5e-5

2.1e-4

0.73

a.

55

Table IV.4 Upright upper subalpine conifer shoot lengths

Data are based on 2011 and 2012 measurements of five shoot lengths (mm) from

each of 10 trees per species in 2011 and 20 trees per species in 2012. Results are shown

for a, Divide Mountain and b, Line Creek RNA. P-values and test statistics shown are the

result of a Tukey’s post hoc analysis following significance from a One-Way ANOVA.

a.

b.

Divide Mountain

Year Length

Spp., Mean (SE)

n Species

Comparisons

t -

statistic

df P –

Value

2011 WP 54.64 (1.6)

ES 33.55 (3.5)

SF 27.06 (3.2)

20

20

20

WB > ES

WB > SF

SF = ES

5.44

7.73

1.37

38

38

38

5.8e-5

9.0e-7

0.27

2012 WP 67.12 (3.0)

ES 32.66 (1.9)

SF 23.21 (1.4)

20

20

20

WB > ES

WB > SF

ES = SF

9.76

13.42

3.96

38

38

38

<0.0001

<0.0001

0.01

Line Creek RNA

Year Length

Spp., Mean (SE)

n Species

Comparisons

t -

statistic

df P –

Value

2011 WP 69.92 (4.1)

ES 34.64 (3.2)

SF 38.4 (3.3)

20

20

20

WB > ES

WB = SF

SF > ES

6.72

5.95

-0.81

38

38

38

5.0e-7

0.74

3.5e-6

2012 WP 70.16 (1.4)

ES 26.24 (1.3)

SF 23.99 (1.3)

20

20

20

WB > ES

WB > SF

ES = SF

23.55

24.10

1.22

38

38

38

<0.0001

<0.0001

0.46

b.

56

Table IV.5 Upright shoots with minimum needle lengths subtracted

Data are based on 2011 and 2012 measurements upright tree shoot lengths with

the minimum needle lengths (as described in Flora of North America) subtracted. All P –

values shown are Tukey’s post hoc results following significance of a one-way ANOVA

at a. Divide Mountain and b. Line Creek RNA.

a.

b.

Line Creek RNA

Year Length

Spp., Mean (SE)

n Species

Comparisons

t -

statistic

df P –

Value

2011 WP 54.64 (1.6)

ES 33.55 (3.5)

SF 27.06 (3.2)

20

20

20

WB > ES

WB > SF

SF = ES

4.05

3.68

-0.38

38

38

38

<0.001

0.001

0.935

2012 WP 67.12 (3.0)

ES 32.66 (1.9)

SF 23.21 (1.4)

20

20

20

WB > ES

WB > SF

ES = SF

16.04

17.84

2.30

38

38

38

<0.001

<0.001

0.069

Divide Mountain

Year Length

Spp., Mean (SE)

n Species

Comparisons

t -

statistic

df P –

Value

2011 WP 54.64 (1.6)

ES 33.55 (3.5)

SF 27.06 (3.2)

20

20

20

WB > ES

WB > SF

SF = ES

1.83

4.37

1.80

38

38

38

0.211

0.002

0.113

2012 WP 67.12 (3.0)

ES 32.66 (1.9)

SF 23.21 (1.4)

20

20

20

WB > ES

WB > SF

ES = SF

5.79

9.76

4.80

38

38

38

<0.001

<0.001

0.001

b.

57

Table IV.6 Small shoot lengths vs. upright shoot lengths: proportions

Data are based on 2011 and 2012 measurements of small trees and upright trees.

Mean (SE) is shown for each sample population. The proportion of small shoots to

upright shoots is shown for a. Divide Mountain and b. Line Creek RNA.

2011

Small Tree

Mean (SE)

Upright Tree

Mean (SE)

Proportion

Small to Up

WP 22.02 (2.9) 54.64 (1.6) 0.403

ES 8.8 (0.85) 33.55 (3.5) 0.262

SF 11.3 (1.6) 27.06 (3.2) 0.419

2012

WP 28.66 (3.4) 67.12 (3.0) 0.427

ES 9.29 (0.9) 32.66 (1.9) 0.284

SF 10.97 (1.1) 23.21 (1.4) 0.473

2011

Small Tree

Mean (SE)

Upright Tree

Mean (SE)

Proportion

Small to Up

WP 48.08 (4.2) 69.92 (4.1) 0.688

ES 22.87 (2.4) 34.63 (3.2) 0.660

SF 27.56 (3.8) 38.40 (3.3) 0.718

2012

WP 48.18 (3.7) 70.16 (1.4) 0.687

ES 21.65 (3.7) 26.24 (1.3) 0.825

SF 25.56 (4.3) 23.99 (1.3) 1.065

a.

b.

58

Figure IV.2 One year post planting seedling survival per microsite

The total number of seedlings that survived for each microsite type is shown for

the Line Creek RNA (dark grey), and Divide Mountain (light grey).

59

Figure IV.3 2012 Divide Mountain seed germination counts

Seedling microsites were visited in July to observe initial germination and again in

September 2012 to observe summer drought mortality. While all microsite types

experienced some mortality, conifer germinants leeward of whitebark microsites tended

to have a lower chance of mortality while those in open exposed conditions experienced

the highest mortality. This indicates whitebark microsites may provide more favorable

conditions for seedling establishment.

Table IV.7 Summer 2012 survival advantage and relative death risk of seed

germinants on Divide Mountain

Using Chi-square expected microsite survival totals compared to actual survival

totals per microsite we calculated the relative survival advantage and death risk for each

microsite compared to each other. Of the four types, whitebark microsites are associated

with the highest survival advantage and lowest risk of death. Open microsites have the

lowest survival advantage and highest risk of death.

Microsite Relative Survival Advantage Relative Risk of Death

Whitebark 5.70 0.18

Spruce 0.89 1.12

Rock 1.08 0.93

Open 0.64 1.56

60

Figure IV.4 Girdling Study leeward conifer shoot length trends over time

Three year leeward conifer shoot length means for both treatment (girdled) and

control (non-girdled) sites at Whitecalf Mountain and Divide Mountain. Trends generally

reflect a decrease in mean shoot lengths over time, with treatment sites experiencing

shorter shoot lengths than control sites. Note: Control sites where the windward

whitebark died during the study were removed from this comparison.

2010 2011 2012

0

5

10

15

20

25

Year

Me

an

Sh

oo

t L

en

gth

(m

m)

Control

Experimental

61

CHAPTER V.

SYNTHESIS AND DISCUSSION

Study Conclusions. The overall objectives of this research were to determine

experimentally and empirically the attributes and ecological interactions that enable

whitebark pine to facilitate tree island development, and to address how the mortality of

whitebark pine from blister rust may impact these ecosystem functions. We tested three

hypotheses focused on learning about whitebark pine’s facilitative functions: 1)

Whitebark pine is hardier than other alpine treeline ecotone conifer species, as

demonstrated by more rapid shoot growth and higher survival at treeline; 2) whitebark

pine provides a more favorable microsite for tree island recruitment than other common

alpine treeline ecotone microsites; and 3) blister rust mortality of whitebark pine in

established tree islands will lead to loss of vigor of leeward conifers. Each of these

hypotheses relates to different aspects of whitebark pine’s role in facilitating formation of

tree islands and the maintenance of established tree islands (Figure II.1). The results

from these studies provide new insight into whitebark pine’s role as a keystone and

foundation species at treeline.

First, our results clarify the issues of hardiness concerning the prevalence, and

shoot growth rates of whitebark pine in our two study areas, as illustrated by position #1

in the conceptual model (Figure II.1). This first finding is extremely important, because

whitebark pine in our study area was previously found to be the most common species

initiating multi-tree tree islands (Resler and Tomback, 2008). Our alpine treeline ecotone

study areas are characterized by harsh climatic conditions consisting of high winds, cold

62

temperatures, and direct solar radiation (Marr, 1977; Arno and Hammerly, 1984; Finklin,

1986; Maher et al., 2005). With respect to the first hypothesis, under harsh, treeline

conditions, whitebark pine is the most prevalent conifer growing in exposed sites as a

solitary tree. While seed caching behaviors of Clark’s nutcracker may lead to greater

seed distribution in open areas at treeline, whitebark pine appears better able to germinate

and establish under challenging conditions than both subalpine fir and Englemann spruce.

This is also demonstrated by the proportionally greater number of solitary whitebark

pines found in minimally sheltering niches or non-sheltering microsites compared to

Engelmann spruce or subalpine fir.

Also indicative of survival and vigor is the ability of whitebark pine to grow

longer shoot lengths, thus potentially expanding canopy biomass more rapidly, than the

other common treeline conifers. Conifer shoots are responsive to a variety of

environmental factors, including length of growing season, soil texture, moisture, and

nutrient levels, temperature, photoperiod, tree vigor, and tree species (Kozlowski, 1964).

Identical trends were found in analyses of shoot lengths both for krummholz conifers and

upper subalpine whitebark, spruce, and fir, with whitebark pine shoots longer than spruce

and fir shoots in both growth forms. This suggests that whitebark may have a species

growth advantage in general in the upper subalpine but this is also the case at treeline,

although all implications are not completely clear. The higher growth rates of

krummholz whitebark shoots during summer months in comparison with spruce and fir

also supports this finding. In order to produce longer shoots, whitebark pine must be able

to capitalize on the scarce environmental resources found at treeline and allocate them

into annual growth. There could well be trade-offs in growth that we are not aware of,

63

such as differential shoot to root ratios among the conifer species (Tilman 1988). Our

measurements of increases in stem diameter, canopy area, and height did not show

species differences. This is likely due to the short duration of the study. There may be

differences among species’ growth strategies (i.e., height vs. canopy volume). Longer

shoot lengths of whitebark pine suggest a species strategy for increasing canopy volume.

This trend might be advantageous in harsh treeline environments where upright growth is

often lost by wind and snow blasts, and ground-level canopy growth is favored (Arno and

Hammerly, 1984).

Given the short growing season at treeline, the ability to increase the volume of

photosynthetic biomass appears to support the premise that whitebark pine is hardier.

Whether this can happen may depend on exposure, flagging, water and nutrient

availability, and annual snowpack depth, which provides protection.

Because whitebark pine appears better able to survive and grow in the alpine

treeline ecotone than other conifer species, and is thus more prevalent, it is more likely to

initiate tree islands by acting as a nurse object for less hardy species, such as Engelmann

spruce as stated in hypothesis #2. Spruce seedling survival is facilitated with the

presence of overhead branches and windward protection (Hattenschwiler and Smith,

1999; Germino et al., 2002). Hypothesis #2 predicts that whitebark pine is a better

facilitator or nurse object than Engelmann spruce, rocks, or no object. If it is, than

whitebark pine will be more likely to be a tree island initiator, as depicted in position 2

and the leeward red star in the conceptual model.

Our studies provide some evidence that whitebark microsites facilitate survival of

other conifers better than the other microsites examined. On Divide Mountain, rock

64

microsites better facilitated the initial germination of sown seeds, most likely due to

greater radiant heat. However, seeds germinating in whitebark microsites were

associated with greater survival during the summer months than all other tested

microsites, including Engelmann spruce. While not significant due to small sample sizes,

the same trend was observed on the Line Creek RNA. The summer months represent the

most critical stage for a newly germinated seedling, which must endure periodic drought

and UV radiation exposure. Studies have found minimal seedling mortality over winter

months (Day, 1964; Cui and Smith, 1991), likely because seedlings are covered by

snowpack at that time and have reduced exposure to harsh solar radiation, temperature

extremes, or chilling winds.

Differences in survival for the nursery grown seedlings among the various

microsite types did not show statistical differences, but seedling health and vigor were

greatest when associated with conifer microsites. Of the conifer microsites, whitebark

was the most likely to be associated with excellent seedling vigor, demonstrating that

growing conditions may be more moderate leeward of whitebark as compared to rocks,

Engelmann spruce, and open microsites. Hence, if a seed germinates leeward of a

whitebark pine at treeline, the seedling’s chances of summer mortality are lower and it

will likely have greater growth vigor should it become established.

Every established solitary tree potentially could facilitate the establishment of a

leeward conifer, thus starting a tree island. Whitebark pine in our study areas is the most

common tree island initiator, and may create a more favorable microsite for leeward tree

survival. Once a whitebark pine facilitates the establishment of a leeward conifer, other

conifers may continuously establish on the leeward side of the developing tree island.

65

This cycle of establishment often continues until a large multi-tree island has formed.

Even after a tree island becomes established, the most windward conifer still provides a

sheltering role to the leeward individual(s). However, the importance of the windward

whitebark pine in offering protection to established conifers needs to be demonstrated in

order to fully understand the potential effects of mortality from blister rust, as stated in

hypothesis #3 and represented in the conceptual model in position #3. With blister rust

rapidly killing whitebark pine in the alpine treeline ecotone (3 of 22 control whitebark

died from blister rust over the course of our study), these previously sheltered subalpine

fir and Engelmann spruce individuals experience new exposure to wind, snow, and ice

blasts. By simulating blister rust through girdling and defoliating the windward

whitebark pine, we found that exposed leeward conifers were more likely to experience

decreased qualitative vigor, shorter shoot lengths, and have higher terminal shoot

mortality than control sites with a healthy windward whitebark pine. This windward

shelter may be most important in years with harsh winter conditions, low snowpack, and

cold, high winds.

In summary, we have found evidence for the importance of whitebark pine at

every investigated stage of the tree island development: the hardiness of whitebark pine

as an exposed, solitary tree and its role as a facilitator in providing a sheltering microsite,

the protection of an establishing seedling, and the windward role in established tree

islands. With whitebark pine declining at treeline from canopy damage and mortality

caused by blister rust infection, the ecosystem functions that we have studied here may

well be diminished.

Potential Implications for Whitebark Pine Decline at Treeline

66

Several interactive factors currently threaten the regeneration of whitebark pine

and the health of established whitebark pine within the alpine treeline ecotone. At the

same time these krummholz conifers are primarily experiencing blister rust mortality,

upper subalpine whitebark pine are declining due to blister rust infection, mountain pine

beetle outbreaks, and fire suppression, leading to the successional advancement of

predominantly subalpine fir and Engelmann spruce stands (Tomback and Achuff , 2010;

Tomback et al. 2011). The loss of these reproductively viable upper subalpine

individuals has a direct impact on the alpine treeline ecotone. Since treeline whitebark

pine rarely produce cones with viable seeds, krummholz whitebark pine are regenerated

by upper subalpine elevation whitebark pine seed sources (Malanson et al,. 2007;

Tomback and Resler, 2007). Upper subalpine whitebark pine mortality leads to lower

seed production, and consequently, fewer seeds available for dispersal at treeline by

Clark’s nutcracker (Tomback and Resler, 2007).

Alpine treeline ecotone vegetation dynamics may potentially be impacted by

mortality of treeline whitebark pine and the reduced regeneration due to loss of cone-

bearing upper subalpine individuals (Tomback and Resler, 2007; Resler and Tomback

2008). Since whitebark pine is a dominant alpine treeline ecotone tree island initiator,

fewer tree islands will be facilitated by whitebark (Figure V.1). Potential impacts of

fewer tree islands include increased soil erosion and snowpack melt-off (Smith et al.,

2009; Tomback et al. 2011), possibly leading to changes in downstream hydrology.

Another issue likely to further impact alpine treeline ecotone dynamics is global

climate change. Warmer temperatures are expected to cause an upward shift in treeline

(Millar et al, 2004; Schrag et al., 2008), with an estimated elevation gain of 140 to 700 m

67

(Grace et al, 2002). As treeline moves upward, new tree islands will form when solitary

conifers are able to establish in areas that were previously solely alpine tundra. These

conifers will act as nurse objects by providing windward shelter for leeward conifers,

ultimately leading to the formation of new tree islands at higher elevations. With

declining numbers of whitebark pine from blister rust at treeline and loss of seed

production, there will be less effective facilitation at the uppermost elevations of suitable

conditions to initiate tree islands. This is because whitebark pine is a dominant tree

island initiator through parts of its range. Thus, the decline of whitebark pine may lead to

a reduced ability of treeline as a whole to respond, possibly leading to the perception that

treeline is not moving up or moving more slowly than suitable temperature zones

(Tomback and Resler 2007). Potential implications for this issue are a loss of treeline

biodiversity as species compositions change (i.e., greater proportions of fir and spruce),

and a reduced range of the alpine treeline ecotone community if tree islands do not form

at higher elevations and the subalpine forest moves upwards.

There is a growing recognition of the importance of plant – plant facilitative

interactions in stressful environments, such as treeline ecosystems (Calloway et al., 2002;

Lortie et al., 2004; Brooker et al., 2008). Plant facilitators often germinate in harsh

environments with minimal protection. Once established, they provide favorable

growing conditions such as windward shelter, shade, and moisture retention (i.e.,

snowpack) for other species (Calloway, 1998; Lortie et al., 2004; Baumeister and

Calloway, 2006; Brooker et al., 2008; Batllori et al., 2009). The facilitation offered by

one species often has the potential to benefit more than one other species (Lortie et al.,

2004; Baumeister and Calloway, 2006). Here, we have identified the importance of

68

whitebark pine for the establishment of spruce and fir leading to development of tree

islands, and have thus demonstrated the crucial facilitation role this species plays at

treeline in parts of its range. With whitebark pine currently declining, and climate change

potentially moving treelines upward, these community changes are already taking place

in treeline ecosystems in the Rocky Mountains.

69

Figures and Tables

Figure V.1 Potential consequences of blister rust to alpine treeline dynamics

(modified from Tomback and Resler 2007).

Fewer seeds dispersed to treeline

by nutcrackers: blister rust in

subalpine whitebark pine

Blister rust damages and kills

whitebark pine at treeline

Decline in treeline whitebark pine

Fewer tree islands initiated by

whitebark pine (less facilitation)

Whitebark pine shows little or

no response to global warming

in upper treeline boundary

Reduced ability of treeline to respond (or lag in response time) to

global warming at the upper boundary

70

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77

APPENDIX

I. Small Tree Measurements

This appendix shows summary data for the Relative Vigor Study (hypothesis 1) small

tree measurements.

Table AI.1 Small Tree Measurement Summaries

The 2010 baseline measurements taken for the small trees in the relative vigor

study are shown in the table below. Summaries are presented as means (SE) for each

measurement by species at a. Divide Mountain and b. Line Creek RNA.

Divide Mountain

Species Age (years) Height (cm) Stem Diameter (mm) Canopy Area (cm2)

WP

ES

SF

14.76 (2.8)

12.73 (1.1)

16.73 (2.5)

6.04 (0.85)

6.67 (0.98)

10.80 (2.34)

5.10 (0.92)

6.57 (1.46)

4.79 (0.86)

155.5 (58.7)

102.6 (32.7)

209.7 (88.7)

Line Creek RNA

Species Age (years) Height (cm) Stem Diameter (mm) Canopy Area (cm2)

WP

ES

SF

10.55 (1.2)

15.80 (1.1)

18.4 (0.72)

9.31 (1.13)

11.56 (1.43)

15.42 (1.49)

5.58 (0.73)

7.96 (0.75)

7.76 (0.84)

157.6 (40.1)

192.3 (34.6)

234.6 (45.5)

a.

b.

78

Table AI.2 Divide Mountain Small Tree Measurements by Site

The 2010 baseline measurements taken for the small trees in the relative vigor

study are shown for each individual site at a. Divide Mountain and b. Line Creek RNA

(next page).

Site Name Stem Diameter (mm) Height (cm) Canopy Area (cm2) Age (years)

Engelmann Spruce 1 2.70 5.7 42 8

Engelmann Spruce 2 4.33 5.5 172 9

Engelmann Spruce 3 4.81 5.6 36 11

Engelmann Spruce 4 2.06 4.0 26 8

Engelmann Spruce 5 3.40 4.0 26 9

Engelmann Spruce 6 2.03 3.0 8 8

Engelmann Spruce 7 2.33 4.2 10 12

Engelmann Spruce 8 2.81 3.0 43 11

Engelmann Spruce 9 3.36 8.0 105 18

Engelmann Spruce 10 1.86 3.0 31 17

Engelmann Spruce 11 3.29 5.0 22 11

Engelmann Spruce 12 9.78 11 217 15

Engelmann Spruce 13 12.70 11 452 19

Engelmann Spruce 14 8.70 15 280 15

Engelmann Spruce 15 7.76 12 70 20

Subalpine Fir 1 1.68 1.7 3 4

Subalpine Fir 2 1.81 2.4 3 8

Subalpine Fir 3 2.60 4.7 16 8

Subalpine Fir 4 2.18 5.2 19 9

Subalpine Fir 5 4.09 8.5 53 14

Subalpine Fir 6 4.13 9.0 7 13

Subalpine Fir 7 4.56 7.0 54 16

Subalpine Fir 8 5.54 8.0 240 19

Subalpine Fir 9 4.85 10 223 15

Subalpine Fir 10 2.77 5.0 12 17

Subalpine Fir 11 18.36 17.5 410 36

Subalpine Fir 12 13.45 26.0 105 27

Subalpine Fir 13 16.26 29.0 1140 28

Subalpine Fir 14 3.28 3.0 14 6

Subalpine Fir 15 13.06 25.0 848 31

Whitebark Pine 1 4.81 4.0 54 10

Whitebark Pine 2 2.35 6.0 3 8.5

Whitebark Pine 3 1.71 4.0 2 2.5

Whitebark Pine 4 2.07 3.4 5 5

Whitebark Pine 5 3.73 4.7 23 9

Whitebark Pine 6 2.43 4.5 35 8

Whitebark Pine 7 1.76 2.0 8 1

Whitebark Pine 8 4.66 5.5 87 15

Whitebark Pine 9 4.30 7.0 86 13

Whitebark Pine 10 2.65 3.5 14 8

Whitebark Pine 11 4.82 6.5 101 29

Whitebark Pine 12 2.82 3.0 36 7

Whitebark Pine 13 10.63 9.5 707 25

Whitebark Pine 14 12.81 7.0 531 26

Whitebark Pine 15 10.50 13.0 678 33

Whitebark Pine 16 2.82 4.0 35 11

Whitebark Pine 17 11.82 15.0 275 40

a.

79

Site Name Stem Diameter (mm) Height (cm) Canopy Area (cm2) Age (years)

Engelmann Spruce 1 6.27 8 99 17

Engelmann Spruce 2 8.50 15 258 26

Engelmann Spruce 3 7.36 10 94 21

Engelmann Spruce 4 6.31 4 56 10

Engelmann Spruce 5 9.33 10 165 14

Engelmann Spruce 6 5.48 7.5 118 21

Engelmann Spruce 7 3.35 4.5 82 10

Engelmann Spruce 8 10.90 14 506 19

Engelmann Spruce 9 12.03 13 212 13

Engelmann Spruce 10 8.05 14 159 16

Engelmann Spruce 11 10.19 23 415 18

Engelmann Spruce 12 3.80 8 90 9

Engelmann Spruce 13 4.79 4 64 15

Engelmann Spruce 14 9.42 13 237 23

Engelmann Spruce 15 9.62 12 163 18

Engelmann Spruce 16 12.19 25.5 560 13

Engelmann Spruce 17 14.03 17 189 15

Engelmann Spruce 18 2.39 3.5 8 7

Engelmann Spruce 19 3.64 5 33 12

Engelmann Spruce 20 11.45 20.5 339 19

Subalpine Fir 1 9.40 20 467 17

Subalpine Fir 2 5.91 15 119 20

Subalpine Fir 3 7.96 11.5 82 18

Subalpine Fir 4 15.03 24 490 23

Subalpine Fir 5 10.56 24 440 21

Subalpine Fir 6 6.55 12.5 363 18

Subalpine Fir 7 7.64 15.5 204 20

Subalpine Fir 8 4.98 11.5 121 20

Subalpine Fir 9 5.74 10 121 16

Subalpine Fir 10 3.96 12.5 94 16

Subalpine Fir 11 7.49 9.5 138 14

Subalpine Fir 12 7.89 19 176 18

Whitebark Pine 1 4.90 6 39 9

Whitebark Pine 2 5.97 7.5 113 14

Whitebark Pine 3 2.42 3 21 6

Whitebark Pine 4 2.63 2.5 22 2

Whitebark Pine 5 3.20 8.5 57 12

Whitebark Pine 6 8.42 12 165 12

Whitebark Pine 7 5.02 5 132 6

Whitebark Pine 8 4.22 6 49 5

Whitebark Pine 9 4.63 9 176 9

Whitebark Pine 10 8.65 12 346 15

Whitebark Pine 11 2.83 7 57 7

Whitebark Pine 12 2.92 6 57 8

Whitebark Pine 13 9.09 16 247 14

Whitebark Pine 14 3.37 11 42 9

Whitebark Pine 15 6.51 10 147 18.5

Whitebark Pine 16 5.72 13 247 13.5

Whitebark Pine 17 7.05 13.5 72 11

Whitebark Pine 18 2.02 6.5 61 5

Whitebark Pine 19 13.52 21.5 785 20

Whitebark Pine 20 1.37 1.5 28 2

Whitebark Pine 21 12.65 18 451 23

b.

80

II. Small Tree Analyses

This appendix shows results from Relative Vigor Study (hypothesis 1) analyses that did

not show statistical differences among species.

Table AII.1 Change in krummholz tree stem diameters

The increase in stem diameters (mm) over the 2010 to 2012 time period is shown

for a, Divide Mountain and b, Line Creek RNA. Analyses were completed with a

Kruskal-Wallis Rank sum test and Wilcox-Signed Rank test with a Bonferroni correction.

All P – values and test statistics shown are from the Wilcox Post Hoc.

a.

b.

Line Creek RNA

Increase

Spp., Mean (SE)

n Species

Comparisons

W -

Statistic

P -

Value

WP 0.99 (0.17)

ES 2.31 (0.46)

SF 2.58 (0.43)

21

20

12

WB < ES

WB < SF

SF = ES

294.5

34

92.5

0.03

0.0006

0.29

Divide Mountain

Increase

Spp., Mean (SE)

n Species

Comparisons

W -

Statistic

P -

Value

WP 0.80 (0.29)

ES 0.98 (0.28)

SF 0.77 (0.37)

17

15

15

WB = ES

WB = SF

SF = ES

122.5

149

95

0.86

0.44

0.47

a.

81

Table AII.2 Krummholz tree canopy areas

Canopy area increases (cm2) from 2010 to 2012 were calculated with a Kruskal-

Wallis Rank sum test and Wilcox-Signed Rank Test with a Bonferroni correction.

Results are shown for a, Divide Mountain and b, Line Creek RNA. All P – values and

test statistics shown are from the Wilcox Post Hoc.

Table AII.3 Krummholz tree heights

Difference between 2012 and 2010 heights (mm) were calculated with a Kruskal-

Wallis Rank sum test and Wilcox-Signed Rank Test with a Bonferroni correction.

Results are shown for a, Divide Mountain and b, Line Creek RNA. All P – values and

test statistics shown are from the Wilcox Post Hoc.

a.

b.

a.

b.

Divide Mountain

Area

Spp., Mean (SE)

n Species

Comparisons

W –

Statistic

P -

Value

WP 56.3 (32.8)

ES -1.65 (0.76)

SF -2.3 (63.5)

17

15

15

WB > ES

WB = SF

SF = ES

140

103

20

0.02

0.95

3.6e-4

Line Creek RNA

Area

Spp., Mean (SE)

n Species

Comparisons

W –

Statistic

P -

Value

WP 196.6 (29.9)

ES 121.3 (18.5)

SF 172.6 (38.4)

21

20

12

WB = ES

WB = SF

SF = ES

272

138

153

0.11

0.67

0.21

Line Creek RNA

Height

Spp., Mean (SE)

n Species

Comparisons

W -

Statistic

P -

Value

WP 3.66 (0.54)

ES 4.3 (0.69)

SF 1.88 (0.77)

21

20

12

WB = ES

WB = SF

SF = ES

204.5

168

163

0.90

0.12

0.10

Divide Mountain

Height

Spp., Mean (SE)

n Species

Comparisons

W -

Statistic

P -

Value

WP 1.06 (0.74)

ES 0.52 (0.37)

SF 1.40 (0.52)

17

15

15

WB = ES

WB = SF

SF = ES

140

103

20

0.78

0.91

0.56

a.

82

III. Planting Study Microsite Heights

This appendix shows the heights for the seedling planting and seed sowing sites by

microsite type.

Table AIII.1 Planting and sowing study microsite heights

The mean and standard deviation (cm) are reported for each microsite type by

seedling and seed sites at a. Divide Mountain and b. Line Creek RNA. All sample sizes

are 20 sites per microsite for both seeds and seedlings at each study area.

Divide Mountain

Site Type Whitebark Spruce Rock Open

Seedlings

Seeds

19.0 (6.4)

14.1 (5.0)

20.9 (6.9)

13.4 (4.3)

15.6 (5.8)

9.9 (2.7)

n/a

n/a

Line Creek RNA

Site Type Whitebark Spruce Rock Open

Seedlings

Seeds

44.3 (10.7)

27.2 (10.8)

44.8 (12.5)

28.6 (8.2)

10.6 (3.8)

7.2 (2.4)

n/a

n/a

a.

b.